import os
import random
import warnings
import time
import copy
import heapq
import joblib
import numpy as np
import pandas as pd
import igraph as ig
import matplotlib.pyplot as plt
import matplotlib
import scipy

from watermark import watermark
from natsort import natsorted
from collections import Counter
from sklearn.feature_selection import (
    chi2,
    f_classif,
    mutual_info_classif,
    RFECV,
    SelectFromModel,
    SelectKBest,
)
from sklearn.metrics import (
    f1_score,
    make_scorer,
    matthews_corrcoef
)
from sklearn.model_selection import (
    GridSearchCV,
    RepeatedStratifiedKFold,
    StratifiedGroupKFold,
)
from sklearn.preprocessing import MinMaxScaler, RobustScaler
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.utils import check_random_state
from pacmap import PaCMAP
from umap import UMAP
from umap.umap_ import (
    find_ab_params,
    nearest_neighbors,
    fuzzy_simplicial_set,
    simplicial_set_embedding,
    dist,
)
from leidenalg import CPMVertexPartition, Optimiser
from networkx import from_scipy_sparse_array

import scanpy as sc
from scanpy.experimental.pp import highly_variable_genes
from anndata import concat

#################
##### genes #####
#################

npr = [
    "Adcyap1r1",
    "Avpr1a",
    "Calcr",
    "Calcrl",
    "Cckar",
    "Cckbr",
    "Cntfr",
    "Crhr1",
    "Crhr2",
    "Esr1",
    "Galr1",
    "Galr2",
    "Galr3",
    "Ghr",
    "Ghrhr",
    "Ghsr",
    "Glp1r",
    "Gpr55",
    "Gpr83",
    "Gpr149",
    "Grpr",
    "Hcrtr1",
    "Hcrtr2",
    "Igf1r",
    "Insr",
    "Insrr",
    "Kiss1r",
    "Lepr",
    "Mc1r",
    "Mc3r",
    "Mc4r",
    "Mchr1",
    "Nmbr",
    "Nmur1",
    "Nmur2",
    "Npffr1",
    "Npffr2",
    "Npr1",
    "Npr2",
    "Npr3",
    "Npsr1",
    "Npsr2",
    "Npy1r",
    "Npy2r",
    "Npy5r",
    "Ntrk2",
    "Ntsr1",
    "Ntsr2",
    "Oprd1",
    "Oprk1",
    "Oprl1",
    "Oprm1",
    "Oxtr",
    "Prlhr",
    "Prlr",
    "Prokr2",
    "Qrfpr",
    "Rxfp1",
    "Rxfp2",
    "Sstr1",
    "Sstr2",
    "Sstr3",
    "Tacr1",
    "Tacr3",
    "Trhr",
    "Trhr2",
    "Tshr",
    "Vipr1",
    "Vipr2",
]

npep = [
    "Adcyap1",
    "Agrp",
    "Avp",
    "Bdnf",
    "Cartpt",
    "Cck",
    "Cntf",
    "Crh",
    "Gal",
    "Ghrh",
    "Ghrl",
    "Grp",
    "Hcrt",
    "Kiss1",
    "Lep",
    "Nmb",
    "Nms",
    "Nmu",
    "Npvf",
    "Npw",
    "Npy",
    "Nts",
    "Oxt",
    "Pdyn",
    "Penk",
    "Pmch",
    "Pnoc",
    "Pomc",
    "Qrfp",
    "Reln",
    "Rln1",
    "Rln3",
    "Sst",
    "Tac1",
    "Tac2",
    "Trh",
]

genes_embed = [
    "Abcd1",
    "Abcd2",
    "Abcd3",
    "Acaa1",
    "Acaa2",
    "Acox1",
    "Agt",
    "Aldh1a1",
    "Aldh1l1",
    "Aldoc",
    "Apoe",
    "Aqp4",
    "Caf4",
    "Ckb",
    "Cnr1",
    "Cnr2",
    "Cst3",
    "Dagla",
    "Daglb",
    "Dcc",
    "Decr2",
    "Dnm1",
    "Drp1",
    "Ech1",
    "Egfr",
    "Eno1",
    "Faah",
    "Fgfr3",
    "Fis1",
    "Fos",
    "Fth1",
    "Ftl1",
    "Gfap",
    "Gja1",
    "Gli1",
    "Glul",
    "Hacd2",
    "Hadhb",
    "Hepacam",
    "Hif1",
    "Htra1",
    "Lcat",
    "Lpcat3",
    "Lxn",
    "Mdv1",
    "Mfn1",
    "Mfn2",
    "Mgll",
    "Mief1",
    "Napepld",
    "Ndrg2",
    "Nfia",
    "Opa1",
    "Otp",
    "Pex1",
    "Pex10",
    "Pex12",
    "Pex13",
    "Pex14",
    "Pex16",
    "Pex2",
    "Pex26",
    "Pex3",
    "Pex6",
    "Pla2g7",
    "Plcb1",
    "Pygb",
    "S100a6",
    "S100b",
    "Scd2",
    "Sgcd",
    "Slc1a2",
    "Slc1a3",
    "Slc4a4",
    "Slc6a11",
    "Slc7a10",
    "Slc38a1",
    "Slit1",
    "Slit2",
    "Sox9",
    "Tafa1",
    "Tkt",
    "Trpv1",
]

nmr = [
    "Adra1a",
    "Adra1b",
    "Adra1d",
    "Adra2a",
    "Adra2b",
    "Adra2c",
    "Adrb1",
    "Adrb2",
    "Adrb3",
    "Adrbk1",
    "Adrbk2",
    "Adrm1",  # adrenergic receptors
    "Adora1",
    "Adora2a",
    "Adora2b",
    "Adora3", # adenosine receptors
    "Chrm1",
    "Chrm2",
    "Chrm3",
    "Chrm4",
    "Chrm5",
    "Chrna1",
    "Chrna2",
    "Chrna3",
    "Chrna4",
    "Chrna5",
    "Chrna6",
    "Chrna7",
    "Chrna9",
    "Chrna10",
    "Chrnb1",
    "Chrnb2",
    "Chrnb3",
    "Chrnd",
    "Chrng", # cholinergic receptors
    "Gria1",
    "Gria2",
    "Gria3",
    "Gria4",  # iGlu AMPA receptors
    "Grid1",
    "Grid2",  # iGlu delta receptors
    "Grik1",
    "Grik2",
    "Grik3",
    "Grik4",
    "Grik5",  # iGlu kainate receptors
    "Grin1",
    "Grin2a",
    "Grin2b",
    "Grin2c",
    "Grin2d",
    "Grin3a",
    "Grin3b",  # iGlu NMDA receptors
    "Grm1",
    "Grm5",  # mGluRs 1
    "Grm2",
    "Grm3",  # mGluRs 2
    "Grm4",
    "Grm6",
    "Grm7",
    "Grm8",  # mGluRs 3
    "Gabra1",
    "Gabra2",
    "Gabra3",
    "Gabra4",
    "Gabra5",
    "Gabra6",
    "Gabrb1",
    "Gabrb2",
    "Gabrb3",
    "Gabrg1",
    "Gabrg2",
    "Gabrg3",
    "Gabrd",
    "Gabre",
    "Gabrp",
    "Gabrq",
    "Gabrr1",
    "Gabrr2",
    "Gabrr3",
    "Gabbr1",
    "Gabbr2", # GABA receptors
    "Drd1",
    "Drd2",
    "Drd3",
    "Drd4",
    "Drd5", # dopamine receptors
    "Htr1a",
    "Htr1b",
    "Htr1d",
    "Htr1f",
    "Htr2a",
    "Htr2b",
    "Htr2c",
    "Htr3a",
    "Htr3b",
    "Htr4",
    "Htr5a",
    "Htr5b",
    "Htr6",
    "Htr7", # serotonin receptors
    "Gnas",
    "Gnai1",
    "Gnai2",
    "Gnai3",
    "Gnao1",
    "Gnao2",
    "Gnaq",
    "Gna11",
    "Gna12",
    "Gna13",
    "Gnal",
    "Gnasxl", # G protein alpha subunit
    "Gnb1",
    "Gnb2",
    "Gnb3",
    "Gnb4",
    "Gnb5", # G protein beta subunit
    "Gng2",
    "Gng3",
    "Gng4",
    "Gng5",
    "Gng7",
    "Gng8",
    "Gng10",
    "Gng11",
    "Gng12",
    "Gng13",
    "Gngt1",
    "Gngt2", # G protein gamma subunit
    "P2rx1",
    "P2rx2",
    "P2rx3",
    "P2rx4",
    "P2rx5",
    "P2rx6",
    "P2rx7",
    "P2ry1",
    "P2ry2",
    "P2ry4",
    "P2ry6",
    "P2ry12",
    "P2ry13",
    "P2ry14", # purinergic receptors
    "Ryr1",
    "Ryr2",
    "Ryr3", # ryanodine receptors
]

functions

parameters

######################
##### parameters #####
######################

# Distance Metric to Use
# metric = snakemake.params["metric"]
metric = "euclidean"
# connectivity_model = snakemake.params["connectivity_model"]
connectivity_model = "min_tree"
PLOTS_DIR = os.path.join("output/figures/resolved_subregions_by_microclusters/")
OUT_DIR = os.path.join("data/resolved_subregions_by_microclusters/")

SMALL_SIZE = 7
MEDIUM_SIZE = 8
BIGGER_SIZE = 12

plt.rc("font", size=SMALL_SIZE)  # controls default text sizes
plt.rc("axes", titlesize=SMALL_SIZE)  # fontsize of the axes title
plt.rc("axes", labelsize=MEDIUM_SIZE)  # fontsize of the x and y labels
plt.rc("xtick", labelsize=SMALL_SIZE)  # fontsize of the tick labels
plt.rc("ytick", labelsize=SMALL_SIZE)  # fontsize of the tick labels
plt.rc("legend", fontsize=SMALL_SIZE)  # legend fontsize
plt.rc("figure", titlesize=BIGGER_SIZE)  # fontsize of the figure title
matplotlib.rcParams["font.size"] = 11
matplotlib.rcParams["pdf.use14corefonts"] = True
matplotlib.rcParams["pdf.fonttype"] = 42
matplotlib.rcParams["ps.fonttype"] = 42
matplotlib.rcParams["font.family"] = "sans-serif"
matplotlib.rcParams["font.sans-serif"] = ["Helvetica"]
matplotlib.rcParams["figure.max_open_warning"] = 20000


reseed = 42
random.seed(reseed)
np.random.seed(reseed)
random_state = check_random_state(reseed)

verbose = True
sc.settings.verbosity = 2  # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.settings.figdir = PLOTS_DIR
sc.settings.writedir = OUT_DIR
sc.settings.set_figure_params(
    dpi=120,
    dpi_save=600,
    vector_friendly=True,
    format="pdf",
    transparent=True,
    color_map="gnuplot_r",
)
sc.settings.autoshow = False
sc.settings.autosave = True

min_dist = 1.0
a, b = find_ab_params(1.0, min_dist)
n_components = 2
negative_sample_rate = 10
# k = int(snakemake.params["k"])
k = 10
ks = k + 10
# signature = snakemake.params["substr_sign"]
signature = 100
# cb_fpr = snakemake.params["res"]
cb_fpr = 0.001
max_comm_size = int(2 * ks)
max_comm_size_paga = int(k * ks)
paga_threshold = 0.25
spread = 2
threads = 120
svm_fit_strength = 100

Load data

genes_table = (
    f"data/output/tables/shared_signature/astrocytes_genes-aggregated-{cb_fpr}.tsv"
)
housekeeping = f"data/data/housekeeping_mouse.tsv"
tf_fname = f"/data/data/mm_tfs.csv"

## OUT:
h5ad_out_path = f"data/data/resolved_subregions/hypothalamic-astrocytes-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
picklefile = f"data/data/resolved_subregions/tntest_hypothalamic-astrocytes-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle"
graph = f"data/data/resolved_subregions/graph_hypothalamic-astrocytes-k_{k}-sign_{signature}-amb_{cb_fpr}.graphml"

# h5ad_out_path = snakemake.output["h5ad"]
# picklefile = snakemake.output["picklefile"]
# graph = snakemake.output["graph"]

sign_genes = pd.read_table(genes_table)
sign_genes = sign_genes.sort_values(["Score"], ascending=True).head(signature)

hk_genes1 = []
with open(housekeeping) as file:
    while hk_genes := file.readline():
        hk_genes1.append(hk_genes.rstrip())

tf_genes = []
with open(tf_fname) as file:
    while tf := file.readline():
        tf_genes.append(tf.rstrip())


PRJNA779749_counts = sc.read_h5ad(
    f"data/PRJNA779749/data/class_cello/PRJNA779749-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA847050_counts = sc.read_h5ad(
    f"data/PRJNA847050/data/class_cello/PRJNA847050-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA815819_counts = sc.read_h5ad(
    f"data/PRJNA815819/data/class_cello/PRJNA815819-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA798401_counts = sc.read_h5ad(
    f"data/PRJNA798401/data/class_cello/PRJNA798401-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA723345_counts = sc.read_h5ad(
    f"data/PRJNA723345/data/class_cello/PRJNA723345-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA722418_counts = sc.read_h5ad(
    f"data/PRJNA722418/data/class_cello/PRJNA722418-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA705596_counts = sc.read_h5ad(
    f"data/PRJNA705596/data/class_cello/PRJNA705596-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA679294_counts = sc.read_h5ad(
    f"data/PRJNA679294/data/class_cello/PRJNA679294-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA611624_counts = sc.read_h5ad(
    f"data/PRJNA611624/data/class_cello/PRJNA611624-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA604055_counts = sc.read_h5ad(
    f"data/PRJNA604055/data/class_cello/PRJNA604055-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA548532_counts = sc.read_h5ad(
    f"data/PRJNA548532/data/class_cello/PRJNA548532-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA515063_counts = sc.read_h5ad(
    f"data/PRJNA515063/data/class_cello/PRJNA515063-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)
PRJNA453138_counts = sc.read_h5ad(
    f"data/PRJNA453138/data/class_cello/PRJNA453138-astrocytes_dataset-{cb_fpr}-initial_selection.h5ad"
)

PRJNA779749_counts = PRJNA779749_counts[PRJNA779749_counts.obs["condit"] == 0]
PRJNA847050_counts = PRJNA847050_counts[PRJNA847050_counts.obs["condit"] == 0]
PRJNA815819_counts = PRJNA815819_counts[PRJNA815819_counts.obs["condit"] == 0]
PRJNA798401_counts = PRJNA798401_counts[PRJNA798401_counts.obs["condit"] == 0]
PRJNA723345_counts = PRJNA723345_counts[PRJNA723345_counts.obs["condit"] == 0]
PRJNA722418_counts = PRJNA722418_counts[PRJNA722418_counts.obs["condit"] == 0]
PRJNA705596_counts = PRJNA705596_counts[PRJNA705596_counts.obs["condit"] == 0]
PRJNA679294_counts = PRJNA679294_counts[PRJNA679294_counts.obs["condit"] == 0]
PRJNA611624_counts = PRJNA611624_counts[PRJNA611624_counts.obs["condit"] == 0]
PRJNA604055_counts = PRJNA604055_counts[PRJNA604055_counts.obs["condit"] == 0]
PRJNA548532_counts = PRJNA548532_counts[PRJNA548532_counts.obs["condit"] == 0]
PRJNA515063_counts = PRJNA515063_counts[PRJNA515063_counts.obs["condit"] == 0]
PRJNA453138_counts = PRJNA453138_counts[PRJNA453138_counts.obs["condit"] == 0]

uni_fs = np.array(['0610005C13Rik', 'Lpl', 'Lpp', 'Lrat', 'Lrba', 'Lrch3', '0610043K17Rik', 'Lrguk', 'Lrig1', '1110008P14Rik', 'Lrmda', '1110015O18Rik', 'Lrp1', '1110019D14Rik', 'Lrp1b', '1110051M20Rik', '1110065P20Rik', '1190005I06Rik', 'Lrp8os2', '1300017J02Rik', '1500004A13Rik', 'Lrrc19', 'Lrrc18', '1500011B03Rik', '1500015L24Rik', 'Lrrc36', '1520401A03Rik', '1600010M07Rik', '1600012H06Rik', 'Lrrc43', 'Lrrc45', 'Lrrc4c', '1700001K19Rik', 'Lrrc52', '1700001L05Rik', '1700001L19Rik', 'Lrrc57', 'Lrrc6', '1700003D09Rik', '1700003G18Rik', 'Lrrc7', '1700003M07Rik', 'Lrrc71', '1700003I16Rik', 'Lrrc8a', '1700008C04Rik', 'Lrrc9', '1700011D18Rik', 'Lrrd1', '1700012C14Rik', '1700012D14Rik', 'Lrriq1', 'Lrrk1', '1700012I11Rik', '1700015C15Rik', 'Lrrn3', '1700016D06Rik', 'Lrrtm1', 'Lsamp', 'Lsm10', 'Lsm4', 'Lsm8', 'Lsmem1', '1700022H01Rik', 'Lsp1', '1700024B05Rik', 'Ltb4r1', 'Ltbp1', '1700024I08Rik', '1700025F22Rik', 'Ltf', '1700026J12Rik', 'Luc7l2', 'Luc7l3', 'Lum', '1700026N04Rik', '1700027A15Rik', '1700027I24Rik', 'Luzp2', '1700028E10Rik', 'Lxn', '1700028P14Rik', '1700029H14Rik', '1700029I15Rik', 'Ly6g6d', 'Ly6h', '1700030F04Rik', 'Ly86', '1700030L22Rik', '1700030N03Rik', '1700034I23Rik', '1700037H04Rik', 'Lysmd4', '1700045H11Rik', 'M1ap', '1700054A03Rik', 'Maats1', 'Mab21l2', 'Macf1', '1700060O08Rik', 'Macrod1', 'Macrod2', '1700061G19Rik', 'Mad2l1bp', '1700064H15Rik', 'Maff', 'Mafb', 'Mageb18', '1700081N11Rik', '1700084C06Rik', 'Magi1', 'Magi2', 'Magel2', 'Majin', 'Mal', 'Malat1', '1700095A13Rik', '1700095A21Rik', '1700095J03Rik', '1700095J07Rik', 'Mamdc2', 'Mamdc4', 'Maml2', 'Maml3', '1700102P08Rik', '1700108F19Rik', '1700108J01Rik', 'Mannr', 'Maob', 'Map1a', 'Map1b', 'Map1lc3a', 'Map1lc3b', 'Map2', '1700125G22Rik', 'Map2k4', 'Map2k5', 'Map2k6', 'Map3k11', '1810020O05Rik', 'Map3k21', '1810030O07Rik', 'Map3k5', '1810037I17Rik', '1810049J17Rik', 'Map4', '1810058I24Rik', 'Map4k3', '2010001A14Rik', '2010001K21Rik', 'Map7', '2010007H06Rik', 'Mapk10', '2010204K13Rik', 'Mapk13', 'Mapk15', 'Mapk4', '2010310C07Rik', '2210011C24Rik', 'Mapk8ip2', '2210016F16Rik', '2210408F21Rik', '2210408I21Rik', 'Mapre2', 'Mapt', 'Marc2', 'March10', '2310022B05Rik', '2310030A07Rik', 'Marcks', 'Marcksl1', 'Marf1', '2310030G06Rik', '2310039L15Rik', 'Mark3', 'Masp1', '2310068J16Rik', 'Masp2', 'Mast4', 'Matk', '2410006H16Rik', 'Maz', 'Mbd5', '2510009E07Rik', 'Mbnl1', 'Mbnl2', '2610001J05Rik', '2600006K01Rik', '2610008E11Rik', 'Mboat2', 'Mboat4', 'Mbp', '2610035D17Rik', 'Mbtd1', '2610028H24Rik', 'Mc4r', 'Mcc', '2610307P16Rik', 'Mccc1os', '2610318N02Rik', '2700033N17Rik', 'Mcm10', '2700097O09Rik', 'Mcm6', '2810030D12Rik', '2810410L24Rik', 'Mcu', 'Mcur1', 'Mdfi', 'Mdga2', '2810459M11Rik', 'Mdh1', 'Mdh2', '2900005J15Rik', 'Mdm4', '2900052L18Rik', '2900052N01Rik', 'Mdp1', 'Mea1', 'Mecp2', '2900097C17Rik', '3010003L21Rik', '3110021N24Rik', '3110039M20Rik', 'Med12l', '3110056K07Rik', '3200001D21Rik', '3222401L13Rik', '3300002A11Rik', 'Med28', 'Medag', 'Mef2c', 'Meg3', 'Megf10', 'Megf11', '4632427E13Rik', '4731419I09Rik', 'Meig1', 'Meikin', 'Meis1', 'Meis2', '4732471J01Rik', '4732496C06Rik', 'Meox1', 'Mepce', 'Mertk', 'Met', '4833422C13Rik', 'Metap1d', '4833427G06Rik', 'Metrn', '4833423E24Rik', 'Mettl11b', 'Mettl17', '4921507P07Rik', '4921511C10Rik', 'Mettl24', '4921524L21Rik', '4921536K21Rik', '4921539E11Rik', '4921539H07Rik', 'Mex3a', '4930402H24Rik', '4930404A12Rik', '4930404I05Rik', '4930404N11Rik', 'Mfap5', 'Mfge8', 'Mfhas1', 'Mfn1', '4930405A21Rik', '4930405D11Rik', 'Mfng', 'Mfsd14b', 'Mfsd2a', 'Mfsd3', '4930415C11Rik', '4930415O20Rik', 'Mfsd7a', 'Mga', '4930422M22Rik', '4930425P05Rik', 'Mgam', 'Mgam2-ps', 'Ttll13', 'Mgat4c', 'Mgat5', 'Mgll', '4930432B10Rik', 'Mgst1', 'Mgst3', '4930435F18Rik', '4930439D14Rik', '4930440I19Rik', '4930441H08Rik', 'Mical2', '4930443O20Rik', '4930444A19Rik', '4930445B16Rik', 'Micos10', 'Micos13', '4930445N06Rik', '4930447C04Rik', 'Micu3', 'Mid1', 'Mid1ip1', 'Mien1', '4930451E10Rik', 'Miip', 'Mill2', '4930455B14Rik', '4930467D21Rik', '4930467E23Rik', '4930470G03Rik', 'Mir100hg', 'Mir124-2hg', '4930473A02Rik', 'Mir9-3hg', 'Mir99ahg', 'Mitf', '4930483O08Rik', 'Mkln1', '4930486I03Rik', 'Mlc1', 'Mlf2', '4930502E18Rik', 'Mlh3', 'Mllt10', 'Mllt11', 'Mllt3', '4930512B01Rik', 'Mmd2', '4930517E14Rik', '4930517O19Rik', '4930518C09Rik', 'Mmp11', 'Mmp14', '4930519K11Rik', 'Mmp23', 'Mmp24', 'Mmp25', '4930527E20Rik', 'Mmrn2', '4930529K09Rik', '4930533N22Rik', '4930535L15Rik', 'Mobp', '4930540M05Rik', 'Mogat1', '4930545L23Rik', 'Mok', '4930549C15Rik', 'Morn1', 'Morf4l2', 'Morn2', 'Morn3', '4930554G24Rik', 'Morrbid', '4930554H23Rik', '4930555B11Rik', 'Morn5', '4930556G01Rik', '4930556J24Rik', '4930556N13Rik', 'Mpc1', 'Mpc2', 'Moxd1', '4930558K02Rik', 'Mpg', 'Mphosph9', '4930567K12Rik', 'Mpp4', '4930570B17Rik', 'Mpp6', 'Mpped1', 'Mpped2', '4930578M07Rik', '4930579C12Rik', 'Mpz', '4930579G24Rik', '4930579H20Rik', '4930581F22Rik', 'Mras', '4930589L23Rik', 'Mrgpra6', 'Mrgprx1', '4930594M22Rik.1', '4930599N23Rik', '4931403E22Rik', 'Mroh7', '4931413K12Rik', '4932411K12Rik', 'Mrpl28', 'Mrpl34', '4932416K20Rik', '4932435O22Rik', '4932438A13Rik', '4932438H23Rik', 'Mrpl41', '4932443L11Rik', 'Mrpl42', '4933402D24Rik', 'Mrpl54', '4933403O08Rik', '4933405D12Rik', 'Mrps14', '4933406F09Rik', 'Mrps18b', 'Mrps33', '4933407L21Rik', 'Mrps6', '4933408J17Rik', 'Mrtfb', 'Mrvi1', '4933421A08Rik', 'Ms4a6d', 'Msantd2', '4933426B08Rik', 'Msh6', 'Msi2', '4933430I17Rik', '4933431E20Rik', 'Msmo1', 'Msn', 'Msl3', '4933431K23Rik', 'Msrb1', '4933433H22Rik', '4933436E23Rik', 'Msx2', 'Mt1', 'Mt2', 'Mt3', 'Mta1', '5031425E22Rik', 'Mta3', '5031439G07Rik', '5031434O11Rik', 'Mtch1', '5033417F24Rik', 'Mtcp1', '5033426O07Rik', '5033430I15Rik', 'Mtf2', 'Mtfp1', '5330438D12Rik', 'Mtfr2', 'Mtg1', '5430416N02Rik', 'Mthfs', '5430427M07Rik', '5430431A17Rik', 'Mtmr3', '5730419F03Rik', 'Mtmr4', '5730522E02Rik', '5830444B04Rik', 'Mtss2', 'Mttp', 'Mtus1', '5930435M05Rik', '6030443J06Rik', '6030498E09Rik', '6330403K07Rik', 'Mucl2', 'Mus81', 'Mvd', '6430503K07Rik', '6330576A10Rik', '6430531B16Rik', '6430553K19Rik', 'Mxra7', '6430571L13Rik', 'Myadm', '6430628N08Rik', '6530402F18Rik', '6530403H02Rik', '6530409C15Rik', '6530411M01Rik', 'Mybpc2', 'Myc', '6720427I07Rik', 'Mycbp', 'Mycbpap', 'Mycl', 'Mycn', '6820408C15Rik', '7420701I03Rik', '8030453O22Rik', 'Myh6', 'Myh7', 'Myl12a', 'Myl12b', 'Myl6', 'Myl7', 'Mylip', '9130008F23Rik', 'Mylk', 'Mylk2', '9130017K11Rik', 'Mylk4', '9130024F11Rik', 'Myo18a', '9130221H12Rik', '9130409I23Rik', 'Myo1f', '9230105E05Rik', 'Myo5b', '9230112E08Rik', 'Myo6', 'Myo7a', 'Myo7b', 'Myo9a', 'Myoc', 'Myof', 'Myorg', '9330158H04Rik', '9330159F19Rik', 'Myrfl', 'Myrip', '9330199G10Rik', 'Naa10', '9430041J12Rik', 'Naa16', 'Naa38', '9530026P05Rik', '9530053A07Rik', 'Naca', 'Nacad', '9630001P10Rik', '9630014M24Rik', 'Naip1', '9630028H03Rik', 'Nalcn', 'Nanos2', '9930021J03Rik', '9930111J21Rik2', 'Nap1l5', 'A130051J06Rik', 'Napb', 'A1bg', 'A230001M10Rik', 'A230020J21Rik', 'A230051N06Rik', 'A230057D06Rik', 'A230059L01Rik', 'Nat8f1', 'Nat8f3', 'Nat8f7', 'Nat8l', 'Nav2', 'Nav3', 'A2m', 'Naxe', 'A230108P19Rik', 'Nbdy', 'A330023F24Rik', 'Nbea', 'Nbl1', 'Ncald', 'Ncam1', 'Ncam2', 'Ncan', 'A330076C08Rik', 'Ncaph', 'A330087D11Rik', 'A330094K24Rik', 'Ncdn', 'Nckap5', 'A430108G06Rik', 'Ncoa1', 'A4galt', 'Ncoa2', 'A530001N23Rik', 'Ncoa6', 'Ncoa7', 'Ncor2', 'Ncs1', 'A530053G22Rik', 'Ndn', 'Ndnf', 'A630001O12Rik', 'A630012P03Rik', 'Ndrg2', 'A630023A22Rik', 'A630023P12Rik', 'A630089N07Rik', 'Ndufa11', 'Ndufa12', 'Ndufa13', 'Ndufa3', 'Ndufa4', 'A730017L22Rik', 'Ndufa6', 'A730020M07Rik', 'Ndufa7', 'Ndufab1', 'A730049H05Rik', 'Ndufaf4', 'A730063M14Rik', 'Ndufb1-ps', 'Ndufb11', 'Ndufb2', 'Ndufb3', 'Ndufb5', 'Ndufb9', 'Ndufc2', 'A730085K08Rik', 'Ndufs7', 'A830035A12Rik', 'Ndufv3', 'Nebl', 'Necab3', 'Necap1', 'A930002C04Rik', 'Nectin1', 'A930006K02Rik', 'Nedd4', 'Nedd4l', 'Nedd8', 'Nefh', 'Nefl', 'Negr1', 'Nek11', 'A930033M14Rik', 'A930037H05Rik', 'A930038B10Rik', 'Nek5', 'Nek2', 'Nek6', 'Nek8', 'AA465934', 'AB124611', 'Nell2', 'AC113595.1', 'AC125149.3', 'Nenf', 'Neo1', 'AC142100.1', 'AC149090.1', 'AC163638.2', 'Neurl1a', 'Neurl1b', 'AC171003.1', 'AI413582', 'Nfat5', 'AI429214', 'Nfatc2', 'AI463229', 'AI480526', 'Nfatc3', 'AI504432', 'AI506816', 'Nfia', 'Nfib', 'Nfic', 'AI849053', 'AK157302', 'Nfkbia', 'Nfkbib', 'Nfkbid', 'Nfkbie', 'Nfkbil1', 'Nfkbiz', 'Nfyc', 'AU023762', 'Ngb', 'Ngef', 'AW047730', 'Nhlh2', 'AW146154', 'Nhs', 'Nhsl1', 'AW554918', 'AY036118', 'Nifk', 'Nim1k', 'Aak1', 'Nipal3', 'Nipbl', 'Aarsd1', 'Aasdhppt', 'Aass', 'Aatk', 'Abat', 'Nipsnap1', 'Nkain1', 'Nkain2', 'Nkain4', 'Nkd1', 'Nkg7', 'Nktr', 'Nkx2-1', 'Nkx2-2', 'Nkx2-2os', 'Nkx2-4', 'Abca9', 'Abcb1a', 'Nkx6-2', 'Nlgn1', 'Nkx6-1', 'Abcb9', 'Nlk', 'Nlgn2', 'Abcc10', 'Abcc12', 'Abcc2', 'Abcc5', 'Nlrp1b', 'Abcd2', 'Abcf2', 'Nme1', 'Nme2', 'Nme3', 'Nme9', 'Abhd14b', 'Abhd15', 'Nmnat2', 'Nmral1', 'Abhd18', 'Abhd3', 'Abhd4', 'Nmur1', 'Nmur2', 'Abi1', 'Nnat', 'Nnt', 'Abl1', 'Abl2', 'Ablim1', 'Ablim2', 'Abo', 'Abr', 'Nol3', 'Nol4', 'Abt1', 'Acaca', 'Nos1ap', 'Notch1', 'Acap1', 'Acap3', 'Notum', 'Acat2', 'Acat3', 'Nova1', 'Nox1', 'Acbd7', 'Npas2', 'Npas3', 'Npas4', 'Ache', 'Npb', 'Ackr3', 'Npc2', 'Ackr4', 'Acot11', 'Nphs1', 'Acot4', 'Nphs2', 'Acot7', 'Npm1', 'Acp1', 'Acsbg1', 'Acsbg2', 'Nptx1', 'Nptx2', 'Acsl3', 'Nptxr', 'Npy', 'Acsm3', 'Acss1', 'Acss2', 'Acss3', 'Nr1d1', 'Nr1d2', 'Actb', 'Actg1', 'Acta1', 'Nr1i2', 'Actn1', 'Nr2f2', 'Nr3c1', 'Nr3c2', 'Nr4a1', 'Actr3b', 'Nr4a2', 'Nr4a3', 'Acvr1c', 'Nrarp', 'Acy3', 'Nrbp2', 'Nrcam', 'Adam11', 'Nrf1', 'Nrg2', 'Adam18', 'Nrgn', 'Adam22', 'Adam23', 'Nrip3', 'Nrk', 'Nrp1', 'Adam5', 'Nrsn1', 'Nrsn2', 'Adamts1', 'Nrxn1', 'Adamts12', 'Nrxn3', 'Nsd2', 'Nsdhl', 'Nsf', 'Adamts2', 'Adamts20', 'Nsmce1', 'Nsmce2', 'Adamts6', 'Adamts5', 'Nsmce3', 'Adamtsl4', 'Nt5c2', 'Adarb1', 'Nt5dc1', 'Nt5dc2', 'Adat2', 'Ntm', 'Adcy2', 'Ntn1', 'Ntn3', 'Adcy5', 'Ntn5', 'Ntng1', 'Adcy8', 'Ntng2', 'Ntrk2', 'Adcyap1r1', 'Ntrk3', 'Nts', 'Adgb', 'Ntsr2', 'Nuak2', 'Adgrb1', 'Adgrb2', 'Adgrb3', 'Adgre4', 'Nudc', 'Adgrf4', 'Adgrf5', 'Adgrg1', 'Adgrg2', 'Adgrg3', 'Adgrl1', 'Adgrl2', 'Adgrl3', 'Nudt15', 'Adgrv1', 'Nudt16l1', 'Adig', 'Nudt4', 'Adm', 'Adra1b', 'Adra2a', 'Adrb1', 'Nup210l', 'Aebp1', 'Nupr1', 'Nupr1l', 'Afdn', 'Aff3', 'Nwd1', 'Nwd2', 'Afm', 'Nxn', 'Agap1', 'Agap2', 'Nxpe4', 'Agbl1', 'Nxpe5', 'Agbl4', 'Agl', 'Agmo', 'Oacyl', 'Oat', 'Agt', 'Obox3', 'Ahctf1', 'Ahcyl1', 'Ahcyl2', 'Ahdc1', 'Ahi1', 'Ocrl', 'Odf2', 'Odf3b', 'Ahnak', 'Odf3', 'Ahsa1', 'Ogdhl', 'Aifm3', 'Ogfod2', 'Ogfod3', 'Ogn', 'Ogt', 'Aip', 'Ajm1', 'Olfm1', 'Olfm2', 'Ak5', 'Ak7', 'Olfml1', 'Ak9', 'Olfm4', 'Olfml2b', 'Akap10', 'Akap11', 'Akap12', 'Akap14', 'Olfr1348', 'Akap6', 'Akap9', 'Olfr1507', 'Olfr1508', 'Akr1a1', 'Olfr1511', 'Akr1b3', 'Olfr1512', 'Akr1c13', 'Olfr275', 'Olfr455', 'Akt2', 'Akt3', 'Alcam', 'Olfr653', 'Aldh1a1', 'Olfr655', 'Olfr656', 'Olfr658', 'Olfr66', 'Olfr731', 'Olfr90', 'Aldoa', 'Olig1', 'Olig2', 'Aldoc', 'Omd', 'Omg', 'Onecut2', 'Opa1', 'Opcml', 'Alkal1', 'Alkal2', 'Opn3', 'Alkbh7', 'Alms1', 'Oprd1', 'Alpl', 'Alyref2', 'Ambra1', 'Amdhd2', 'Amelx', 'Amer2', 'Osbpl9', 'Ammecr1', 'Osmr', 'Amot', 'Osr1', 'Ost4', 'Otc', 'Otoa', 'Amy1', 'Anapc11', 'Anapc13', 'Otud1', 'Otud7a', 'Ang', 'Angel1', 'Otx1', 'Otx2', 'Otx2os1', 'Ovca2', 'Angptl4', 'Ank', 'Ank2', 'Ank3', 'Oxt', 'Ankfn1', 'Ankhd1', 'P2rx3', 'P2ry1', 'P2ry12', 'P2ry13', 'P2ry14', 'Ankrd12', 'P2ry6', 'Ankrd2', 'Ankrd22', 'P3h4', 'Ankrd34a', 'Pabpn1', 'Pacrg', 'Pacsin1', 'Pacsin2', 'Padi2', 'Paf1', 'Ankrd55', 'Ankrd6', 'Anks1', 'Anks1b', 'Pak3', 'Ankub1', 'Ankzf1', 'Anln', 'Pam16', 'Pan3', 'Pank1', 'Antxr1', 'Pantr1', 'Pantr2', 'Panx1', 'Panx2', 'Paox', 'Anxa13', 'Anxa2', 'Anxa4', 'Anxa5', 'Pappa', 'Anxa9', 'Aopep', 'Aox1', 'Paqr6', 'Paqr8', 'Aox2', 'Pard3', 'Pard3b', 'Pard3bos1', 'Ap1s2', 'Parm1', 'Ap2s1', 'Parp8', 'Parvb', 'Ap5s1', 'Apba2', 'Apbb2', 'Apc', 'Apc2', 'Pawr', 'Pax1', 'Pax5', 'Paxbp1', 'Apln', 'Pbk', 'Aplp1', 'Apmap', 'Pbx1', 'Apob', 'Apobec2', 'Apobec4', 'Apoc1', 'Pcca', 'Apod', 'Apoe', 'Pcdh11x', 'Apoh', 'Pcdh17', 'Pcdh18', 'Pcdh7', 'Pcdh9', 'App', 'Appl2', 'Aqp4', 'Aqp5', 'Pcdhb2', 'Aqp9', 'Ar', 'Araf', 'Pcdhb22', 'Arap2', 'Arc', 'Arf2', 'Arf3', 'Pced1b', 'Arf5', 'Arf6', 'Pcgf1', 'Arfgef1', 'Pck2', 'Arfgef3', 'Pclo', 'Pcm1', 'Arg2', 'Arglu1', 'Arhgap17', 'Arhgap21', 'Pcp4', 'Arhgap23', 'Pcp4l1', 'Pcsk1n', 'Arhgap26', 'Pcsk2', 'Pcsk2os1', 'Arhgap27os2', 'Pcsk4', 'Arhgap29', 'Arhgap31', 'Arhgap32', 'Pcsk9', 'Pcx', 'Arhgap35', 'Arhgap33', 'Arhgap36', 'Pcyt2', 'Arhgap40', 'Arhgap5', 'Arhgap6', 'Pdcd1lg2', 'Arhgdib', 'Arhgdig', 'Arhgef10', 'Arhgef10l', 'Arhgef11', 'Arhgef12', 'Pde10a', 'Pde1b', 'Arhgef18', 'Pde3a', 'Arhgef26', 'Arhgef28', 'Pde4d', 'Pde4b', 'Arhgef4', 'Pde6g', 'Pde7b', 'Arhgef9', 'Arid1a', 'Pde8b', 'Pde9a', 'Arid3a', 'Pdgfb', 'Pdgfd', 'Pdgfrb', 'Arl13a', 'Arl15', 'Pdia4', 'Arl4aos', 'Arl4d', 'Pdlim1', 'Arl6ip1', 'Pdlim4', 'Pdlim7', 'Armc2', 'Armc3', 'Armc4', 'Armc9', 'Pdss2', 'Pdxp', 'Armcx4', 'Pdyn', 'Pdzd7', 'Arnt2', 'Arntl', 'Pdzph1', 'Arpc1a', 'Pdzk1ip1', 'Arpc2', 'Peak1', 'Arpc3', 'Pea15a', 'Pebp1', 'Arpc1b', 'Arpp19', 'Arpp21', 'Peak1os', 'Pebp4', 'Peg13', 'Peg3', 'Arsj', 'Per2', 'Per3', 'Art2b', 'Arx', 'Arvcf', 'Asap1', 'Asb14', 'Pfdn1', 'Pfdn2', 'Asb17os', 'Asb2', 'Pfkfb4', 'Pfn1', 'Pfn2', 'Ascl4', 'Pgam1', 'Asic1', 'Asic2', 'Asic3', 'Asic4', 'Asna1', 'Pgk1', 'Asns', 'Pglyrp1', 'Pgm1', 'Pgp', 'Pgpep1l', 'Astn1', 'Astn2', 'Pgrmc1', 'Phactr1', 'Phactr2', 'Phactr3', 'Pgs1', 'Atad2b', 'Ate1', 'Phf10', 'Atf3', 'Atf7', 'Atf7ip', 'Phf21a', 'Phf21b', 'Phf23', 'Phf24', 'Phf3', 'Phgdh', 'Phka1', 'Phkb', 'Phkg1', 'Atic', 'Phlda1', 'Atl2', 'Phldb2', 'Atoh7', 'Phlpp1', 'Atox1', 'Phpt1', 'Atp13a4', 'Atp13a5', 'Phyhipl', 'Atp1a2', 'Atp1a3', 'Atp1a1', 'Atp1b1', 'Atp1b2', 'Pi15', 'Pi16', 'Pianp', 'Atp2a2', 'Atp23', 'Pid1', 'Atp2b2', 'Piezo1', 'Piezo2', 'Atp5b', 'Atp5d', 'Atp5e', 'Atp5f1', 'Atp5g1', 'Atp5g2', 'Atp5g3', 'Atp5h', 'Atp5j', 'Atp5j2', 'Atp5k', 'Atp5l', 'Atp5md', 'Atp5mpl', 'Atp5o', 'Atp6v0a1', 'Pih1h3b', 'Atp6v0b', 'Atp6v0c', 'Pik3c2g', 'Atp6v0e', 'Atp6v1b2', 'Pik3r1', 'Atp6v1c2', 'Atp6v1e2', 'Atp6v1g1', 'Atp6v1g2', 'Atp8a1', 'Pink1', 'Pip4k2a', 'Atp8b4', 'Atp9b', 'Pip5k1b', 'Atpif1', 'Pipox', 'Atr', 'Atraid', 'Atrnl1', 'Atxn1', 'Atxn2', 'Atxn2l', 'Pitpnc1', 'Pitpnm2', 'Pitpnm2os1', 'Pitpnm3', 'Pitpnm2os2', 'Atxn7l2', 'Pitx2', 'Atxn7l3', 'Piwil4', 'Pja2', 'Auts2', 'Avil', 'Pkd1l2', 'Avp', 'Avpi1', 'Pkd1l3', 'Pkdcc', 'Pkhd1', 'Pkib', 'B130024G19Rik', 'Pknox2', 'Pla1a', 'Pla2g10', 'Pla2g2c', 'B230216N24Rik', 'Pla2g4a', 'B230307C23Rik', 'Pla2g7', 'Pla2r1', 'B230323A14Rik', 'Plac8l1', 'Plac9a', 'B2m', 'B3galnt1', 'Plat', 'B3galt1', 'B3galt2', 'Platr27', 'Platr8', 'Plb1', 'Plbd1', 'Plcb1', 'B430010I23Rik', 'Plcb3', 'Plcb4', 'Plce1', 'B4galnt1', 'Plch2', 'Plcl1', 'Plcl2', 'Plcxd3', 'B630019K06Rik', 'Pld5', 'B830042I05Rik', 'B930036N10Rik', 'Plek2', 'Plekha6', 'BB365896', 'Plekhb1', 'BB557941', 'Plekhg1', 'Plekhg2', 'BC017158', 'Plekhg4', 'Plekhm2', 'BC031181', 'BC034090', 'Plekho2', 'BC037032', 'Plin3', 'Plk2', 'BC049715', 'Pllp', 'BC050972', 'BC051019', 'Plp1', 'BC051226', 'BC065397', 'BX470216.1', 'Plppr2', 'Baalc', 'Plppr3', 'Plppr5', 'Plppr4', 'Bach2', 'Plscr4', 'Bach2os', 'Bag1', 'Plut', 'Bag3', 'Plxdc2', 'Bahcc1', 'Baiap2', 'Plxna4', 'Baiap3', 'Plxnb1', 'Bambi', 'Plxnc1', 'Plxnd1', 'Pmch', 'Basp1', 'Pmepa1', 'Barx2', 'Batf3', 'Bax', 'Pmepa1os', 'Bbc3', 'Bbof1', 'Bbox1', 'Pnck', 'Pnisr', 'Bbs1', 'Bbs9', 'Pnmal1', 'Bc1', 'Pnmal2', 'Bcan', 'Bcam', 'Bcar3', 'Bcas1', 'Pnpla2', 'Bcas3', 'Pnrc1', 'Bcas3os1', 'Podn', 'Podnl1', 'Bckdhb', 'Podxl2', 'Bcl11b', 'Bcl2', 'Pogk', 'Bcl2a1b', 'Poglut1', 'Poglut2', 'Pold2', 'Bcl2l2', 'Bcl9', 'Polr1c', 'Polr1d', 'Bdkrb1', 'Bdnf', 'Becn1', 'Begain', 'Polr2f', 'Polr2l', 'Bet1l', 'Bex1', 'Bex2', 'Bex3', 'Polr3h', 'Bgn', 'Pomp', 'Bhmt2', 'Bicd1', 'Bicd2', 'Bicra', 'Bicral', 'Bin1', 'Postn', 'Pot1b', 'Pou2f1', 'Birc5', 'Blcap', 'Pou3f2', 'Pou3f3', 'Pou3f4', 'Bloc1s1', 'Bloc1s2', 'Ppargc1a', 'Bmerb1', 'Bmf', 'Bmi1', 'Ppdpf', 'Bmp5', 'Ppfia4', 'Bmp7', 'Ppia', 'Ppib', 'Ppic', 'Bmpr1b', 'Bmx', 'Bnip2', 'Ppil6', 'Boc', 'Ppip5k2', 'Bod1l', 'Bola2', 'Bola3', 'Ppm1e', 'Ppm1f', 'Bpifb1', 'Ppp1r11', 'Bpifb9b', 'Ppp1r12b', 'Ppp1r12c', 'Ppp1r13b', 'Brd1', 'Ppp1r15a', 'Ppp1r16b', 'Brd3os', 'Ppp1r18', 'Ppp1r1a', 'Ppp1r1b', 'Bri3', 'Ppp1r1c', 'Ppp1r26', 'Brinp3', 'Brip1', 'Brip1os', 'Ppp1r3a', 'Brk1', 'Ppp1r3c', 'Ppp1r3d', 'Ppp1r42', 'Ppp1r9a', 'Ppp1r9b', 'Bsg', 'Ppp2r2b', 'Ppp2r2c', 'Bst1', 'Btaf1', 'Ppp2r3a', 'Bsx', 'Btbd11', 'Btbd17', 'Ppp3ca', 'Btbd3', 'Ppp3r1', 'Btf3', 'Btg2', 'Btg3', 'Ppp6r3', 'Ppp6r1', 'Btk', 'Btrc', 'Pqlc3', 'Bves', 'Prag1', 'Bvht', 'C030005K06Rik', 'Prcd', 'C030007H22Rik', 'Prdm1', 'Prdm12', 'C030018K13Rik', 'Prdm16', 'C030032O16Rik', 'C030034L19Rik', 'Prdm5', 'Prdm8', 'Prdx1', 'Prdx2', 'Prdx4', 'Prdx5', 'Prdx6', 'C130040N14Rik', 'C130071C03Rik', 'Prelid3a', 'C130073E24Rik', 'Prelp', 'Prex1', 'Prex2', 'C1galt1c1', 'Prickle1', 'Prickle2', 'C1qc', 'C1ql3', 'C1qtnf1', 'C1qtnf4', 'Prkaa2', 'C1qtnf6', 'Prkacb', 'C1s1', 'Prkag2os1', 'C2', 'Prkar1b', 'Prkag3', 'Prkca', 'Prkcb', 'Prkcd', 'Prkce', 'C230034O21Rik', 'C230072F16Rik', 'Prkcz', 'C2cd3', 'Prkd1', 'C2cd4c', 'C2cd4d', 'C2cd5', 'Prkn', 'C2cd6', 'C330002G04Rik', 'Prkx', 'C330013E15Rik', 'C430014B12Rik', 'C4b', 'C530008M17Rik', 'C530025M09Rik', 'C530044C16Rik', 'C5ar1', 'Prodh', 'C5ar2', 'C630028M04Rik', 'Prom2', 'C78334', 'Prox2', 'C8g', 'C9', 'Prpf31', 'Prpf39', 'Cab39l', 'Cabcoco1', 'Cabin1', 'Cables1', 'Prr11', 'Prr16', 'Prr19', 'Cacna1a', 'Cacna1d', 'Prr5l', 'Prrc2b', 'Prrc2c', 'Cacna1g', 'Cacna1h', 'Cacna2d1', 'Cacna2d2', 'Cacna2d3', 'Prrt1', 'Prrt2', 'Cacnb2', 'Prrg3', 'Prrx2', 'Prrt3', 'Cacng2', 'Prss22', 'Prss23', 'Cacng5', 'Prss27', 'Prss36', 'Prss41', 'Cadm1', 'Cadm2', 'Prss45', 'Cadm4', 'Cadps', 'Prtg', 'Calb1', 'Calb2', 'Calcr', 'Psap', 'Psd', 'Psd2', 'Calm3', 'Psd3', 'Caln1', 'Calr', 'Psenen', 'Calm1', 'Calml4', 'Caly', 'Psd4', 'Camk1d', 'Psma2', 'Camk2a', 'Camk2b', 'Camk2d', 'Camk2g', 'Camk2n1', 'Camk2n2', 'Psmb1', 'Psmb2', 'Psmb3', 'Camkmt', 'Camkv', 'Psmb5', 'Psmb6', 'Camk4', 'Psmb8', 'Camta1', 'Cap2', 'Capg', 'Psme2', 'Psme2b', 'Psors1c2', 'Psrc1', 'Capns1', 'Capsl', 'Capzb', 'Ptch1', 'Car10', 'Ptch2', 'Ptchd4', 'Ptchd1', 'Car15', 'Car3', 'Ptgds', 'Ptger3', 'Car7', 'Car8', 'Ptgr1', 'Ptgs1', 'Carhsp1', 'Ptgs2', 'Ptgs2os', 'Carmil1', 'Pth1r', 'Ptk2', 'Carmil2', 'Ptma', 'Ptms', 'Cask', 'Casp12', 'Ptpn13', 'Casp6', 'Ptpn20', 'Ptpn4', 'Ptpn5', 'Ptpn7', 'Ptpn9', 'Ptprd', 'Ptpre', 'Ptprg', 'Catsperb', 'Ptprj', 'Catsper4', 'Ptprm', 'Ptprh', 'Catspere1', 'Ptpro', 'Cav2', 'Cavin1', 'Ptprt', 'Cavin3', 'Ptprz1', 'Cbfa2t3', 'Cbln1', 'Cbln2', 'Cbln3', 'Purg', 'Pvalb', 'Cc2d2a', 'Ccbe1', 'Ccdc103', 'Pxylp1', 'Ccdc114', 'Pygm', 'Qdpr', 'Qk', 'Ccdc134', 'Ccdc141', 'Ccdc146', 'Ccdc148', 'Ccdc150', 'Ccdc152', 'Ccdc153', 'R3hdm1', 'Ccdc155', 'Ccdc158', 'Ccdc162', 'Rab11fip2', 'Ccdc166', 'Rab11fip4', 'Ccdc169', 'Rab11fip4os1', 'Ccdc170', 'Ccdc171', 'Rab20', 'Ccdc180', 'Rab26os', 'Ccdc190', 'Ccdc191', 'Rab30', 'Ccdc22', 'Ccdc24', 'Rab32', 'Rab33b', 'Ccdc30', 'Rab3a', 'Ccdc34os', 'Rab3c', 'Ccdc39', 'Ccdc38', 'Ccdc60', 'Rab5c', 'Rab5if', 'Rab6b', 'Ccdc78', 'Rab9b', 'Rabac1', 'Rabep1', 'Rabgap1l', 'Ccdc84', 'Ccdc85a', 'Rabggtb', 'Ccdc85c', 'Ccdc86', 'Ccdc88a', 'Ccdc88b', 'Ccdc88c', 'Ccdc89', 'Ccdc92', 'Rad21l', 'Ccl22', 'Ccl27a', 'Ccn1', 'Ralgapa1', 'Ralgapa2', 'Ralgapb', 'Ccn4', 'Ralgps2', 'Ccn6', 'Ralyl', 'Ramac', 'Ramp1', 'Ccnb1', 'Ran', 'Rap1gap', 'Ccno', 'Rapgef5', 'Rara', 'Ccser1', 'Rarres2', 'Cd109', 'Rasgef1a', 'Cd164l2', 'Rasgef1c', 'Rasgrf1', 'Rasgrf2', 'Rasgrp2', 'Cd200r3', 'Cd200r4', 'Rasl11a', 'Rasl11b', 'Cd24a', 'Cd27', 'Rassf2', 'Cd2ap', 'Rassf9', 'Raver2', 'Rax', 'Cd36', 'Cd38', 'Cd44', 'Cd46', 'Cd47', 'Rbfox2', 'Rbis', 'Cd63', 'Rbm26', 'Cd79a', 'Cd81', 'Cd82', 'Rbm33', 'Cd83', 'Cd9', 'Rbm42', 'Cd96', 'Rbm44', 'Cda', 'Rbm4b', 'Rbm5', 'Rbm6', 'Rbms2', 'Rbms3', 'Rbmxl1', 'Rbp1', 'Cdc42bpa', 'Cdc42ep1', 'Rbpms', 'Cdc42ep4', 'Rbx1', 'Rbsn', 'Cdc42ep5', 'Cdc73', 'Cdca8', 'Rcn1', 'Cdh1', 'Cdh10', 'Cdh11', 'Rcor2', 'Rcsd1', 'Rcvrn', 'Cdh19', 'Cdh2', 'Cdh20', 'Cdh23', 'Reck', 'Cdhr3', 'Cdhr4', 'Recql5os1', 'Cdk1', 'Cdk10', 'Cdk12', 'Cdk13', 'Cdk14', 'Relch', 'Cdk18', 'Rell2', 'Reps1', 'Cdk5r1', 'Resp18', 'Cdk8', 'Cdkal1', 'Cdkl3', 'Cdkl4', 'Rev1', 'Rev3l', 'Rex1bd', 'Cdo1', 'Cdr1', 'Cdr1os', 'Rfx3', 'Rfx4', 'Cdsn', 'Ceacam1', 'Rfxap', 'Cebpb', 'Rgl1', 'Cecr2', 'Rgl2', 'Rgma', 'Celf2', 'Cela1', 'Celf4', 'Celf3', 'Celf5', 'Celrr', 'Celf6', 'Cemip', 'Rgs2', 'Rgs20', 'Rgs19', 'Rgs22', 'Cenpa', 'Rgs4', 'Rgs6', 'Rgs7', 'Rgs7bp', 'Cenph', 'Rgs8', 'Cenpt', 'Rhcg', 'Rhd', 'Cep112', 'Rheb', 'Cep126', 'Cep128', 'Rhoa', 'Rhobtb1', 'Rhobtb2', 'Rhob', 'Rhoc', 'Cep170b', 'Rhoq', 'Rhou', 'Cep350', 'Rian', 'Ribc2', 'Rida', 'Rif1', 'Cep85l', 'Rimbp2', 'Rims2', 'Ces1d', 'Ces5a', 'Ripor2', 'Cfap100', 'Rit2', 'Cfap161', 'Cfap206', 'Cfap299', 'Rmdn1', 'Rmi1', 'Cfap43', 'Cfap44', 'Rmnd5a', 'Cfap46', 'Rmst', 'Cfap52', 'Rnd1', 'Cfap54', 'Cfap58', 'Cfap61', 'Cfap65', 'Cfap69', 'Cfap70', 'Rnf121', 'Cfap74', 'Cfap77', 'Rnf122', 'Cfap73', 'Cfap97d2', 'Cfap99', 'Rnf128', 'Cfh', 'Cfap97d1', 'Cfb', 'Cfl1', 'Cfp', 'Rnf152', 'Rnf165', 'Cgnl1', 'Cgref1', 'Rnf169', 'Rnf19b', 'Chchd10', 'Rnf216', 'Chd2', 'Chd3', 'Chd5', 'Chd6', 'Chd7', 'Rnf24', 'Rnf227', 'Chga', 'Rnf5', 'Chil1', 'Chil5', 'Rngtt', 'Chl1', 'Rnpc3', 'Robo1', 'Robo2', 'Chn1', 'Rogdi', 'Romo1', 'Ropn1l', 'Ror1', 'Ror2', 'Rora', 'Rorb', 'Chrac1', 'Chrdl1', 'Rp1', 'Rpe65', 'Chst1', 'Chst10', 'Chst2', 'Chst3', 'Rptor', 'Rrad', 'Rreb1', 'Rrm1', 'Ciart', 'Cib1', 'Rrm2', 'Rrnad1', 'Cic', 'Rrp15', 'Cip2a', 'Cipc', 'Rsf1os1', 'Rsf1os2', 'Cisd1', 'Rsad2', 'Cit', 'Cited1', 'Rslcan18', 'Ckap2', 'Ckb', 'Cklf', 'Ckmt1', 'Ckmt2', 'Rsph6a', 'Rspo2', 'Clasp1', 'Clasp2', 'Rsrp1', 'Clba1', 'Clcf1', 'Rtl4', 'Rtl8a', 'Clcn6', 'Rtl8b', 'Rtn1', 'Clcnka', 'Cldn10', 'Rtp3', 'Cldn18', 'Cldn2', 'Rubie', 'Rufy2', 'Rufy3', 'Rundc3a', 'Runx1t1', 'Clec3b', 'Rxrg', 'Clic1', 'Clic3', 'Clic4', 'Ryr1', 'Clic6', 'Clip1', 'S100a1', 'S100a10', 'S100a11', 'Clk1', 'S100a13', 'S100a16', 'Clmn', 'S100a4', 'S100a6', 'S100a7a', 'S100b', 'S100g', 'S100pbp', 'S1pr1', 'S1pr3', 'S1pr5', 'Sall2', 'Sall3', 'Clstn3', 'Clu', 'Clta', 'Cltb', 'Samd12', 'Samd11', 'Samd4', 'Cmip', 'Cmss1', 'Cmtm2a', 'Sap30bpos', 'Sap30l', 'Saraf', 'Cmtr2', 'Sardhos', 'Pigm', 'Sash1', 'Cndp2', 'Sat1', 'Cnga4', 'Cnih2', 'Saxo1', 'Cnn1', 'Cnn3', 'Sbno2', 'Cnnm2', 'Cnnm3', 'Cnnm1', 'Cnot2', 'Scaf8', 'Cnp', 'Scaper', 'Cnpy2', 'Scara5', 'Scd1', 'Cntf', 'Cntfr', 'Scd2', 'Cntn1', 'Scfd2', 'Scg2', 'Cntd1', 'Scg5', 'Cntn2', 'Cntnap1', 'Scgn', 'Scin', 'Sclt1', 'Scly', 'Scmh1', 'Cntnap5c', 'Coa3', 'Scn2a', 'Scn2b', 'Scn3b', 'Scn5a', 'Scn8a', 'Scn9a', 'Scpep1os', 'Scrg1', 'Scrib', 'Col1a1', 'Col1a2', 'Scrt1', 'Scube1', 'Col23a1', 'Scrt2', 'Col25a1', 'Col3a1', 'Sdc4', 'Sdcbp', 'Sdccag8', 'Col4a3', 'Col4a6', 'Col6a1', 'Col6a2', 'Sdhb', 'Sdk2', 'Col8a2', 'Sds', 'Col9a2', 'Sec1', 'Colec12', 'Sec14l5', 'Sec61b', 'Sec61g', 'Sec62', 'Cops9', 'Selenbp1', 'Selenof', 'Coq10b', 'Selenoh', 'Selenok', 'Selenbp2', 'Selenom', 'Coq6', 'Selenop', 'Selenos', 'Corin', 'Coro1a', 'Selenow', 'Selenov', 'Coro2b', 'Sem1', 'Cotl1', 'Sema3c', 'Cox14', 'Cox17', 'Sema3b', 'Sema3d', 'Sema4a', 'Cox4i1', 'Sema3f', 'Cox5a', 'Cox5b', 'Cox6a1', 'Sema4f', 'Cox6a2', 'Cox6c', 'Cox7a1', 'Cox7a2', 'Cox7b', 'Sema6d', 'Cox8a', 'Sema5a', 'Sema5b', 'Sema7a', 'Senp7', 'Cpe', 'Sept11', 'Cpeb3', 'Cpeb4', 'Cped1', 'Cplane1', 'Sept4', 'Sept2', 'Sept7', 'Sept6', 'Sept9', 'Cpn1', 'Serf2', 'Cpne6', 'Cpne8', 'Serp2', 'Serpina3c', 'Serpina3n', 'Cpq', 'Serpinb5', 'Cpsf4l', 'Serpinb8', 'Serpinb9', 'Serpine1', 'Serpine2', 'Serpine3', 'Cpxm1', 'Cr2', 'Serpinf1', 'Serpinh1', 'Crabp2', 'Serpini2', 'Setbp1', 'Crebbp', 'Crebrf', 'Crebzf', 'Setd2', 'Creg2', 'Creld2', 'Sez6l', 'Sez6l2', 'Crhbp', 'Crim1', 'Sf3b1', 'Crip2', 'Sf3b4', 'Sf3b6', 'Sfi1', 'Sfmbt1', 'Sfn', 'Sfr1', 'Crlf3', 'Crlf2', 'Sfrp1', 'Sfrp5', 'Crocc', 'Crocc2', 'Crmp1', 'Sft2d2', 'Crp', 'Sfta3-ps', 'Sfxn2', 'Crtc3', 'Sfxn5', 'Cry2', 'Sgcd', 'Cryab', 'Sgcz', 'Crybg1', 'Sgip1', 'Crybg2', 'Sgms1', 'Crym', 'Sgsm1', 'Sh2b1', 'Csgalnact1', 'Csmd2', 'Csmd3', 'Sh3bgrl3', 'Cspg5', 'Cspp1', 'Csrnp1', 'Sh3bp5', 'Csrnp3', 'Csrp2', 'Cst3', 'Sh3gl2', 'Sh3glb2', 'Sh3kbp1', 'Cstb', 'Sh3pxd2b', 'Cstdc2', 'Sh3rf2', 'Sh3rf3', 'Sh3tc2', 'Shank1', 'Shank2', 'Shbg', 'Ctdspl', 'Shc3', 'Ctif', 'Shh', 'Ctnna2', 'Shisa3', 'Ctnnb1', 'Shisa6', 'Ctnnd2', 'Shisa9', 'Ctsd', 'Shmt1', 'Ctsh', 'Ctsk', 'Ctsl', 'Shmt2', 'Shprh', 'Shroom2', 'Shroom3', 'Shroom4', 'Ctxn2', 'Siah2', 'Siglec15', 'Sik1', 'Sik3', 'Cutal', 'Cux1', 'Sim2', 'Sipa1l1', 'Sipa1l3', 'Sirpa', 'Cxcl12', 'Cxcl14', 'Cxcl16', 'Cxcr4', 'Cxcr5', 'Sirt7', 'Six3', 'Six3os1', 'Six4', 'Cyb561', 'Cyb561d1', 'Cyb5a', 'Skida1', 'Cyb5r3', 'Cycs', 'Cyfip1', 'Cyfip2', 'Cygb', 'Cyld', 'Cyp27a1', 'Slc12a5', 'Slc12a6', 'Cyp2c50', 'Cyp2c55', 'Slc13a1', 'Slc13a3', 'Cyp2c69', 'Slc13a5', 'Slc14a2', 'Slc15a1', 'Slc15a2', 'Cyp2j6', 'Slc16a11', 'Slc16a2', 'Cyp4f15', 'Slc17a6', 'Slc17a9', 'Cyp4x1', 'Cyp51', 'Slc18a3', 'Slc1a2', 'Slc1a3', 'Cystm1', 'Cyth1', 'Slc1a4', 'Slc20a1', 'Slc20a2', 'Cyyr1', 'Slc22a12', 'Slc22a13', 'D030046N08Rik', 'D030047H15Rik', 'Slc22a2', 'Slc22a20', 'D030068K23Rik', 'Slc22a27', 'Slc22a3', 'D130040H23Rik', 'D130062J10Rik', 'D130079A08Rik', 'D230022J07Rik', 'Slc24a2', 'Slc24a3', 'Slc24a4', 'Slc25a10', 'Slc25a12', 'D3Ertd751e', 'D430019H16Rik', 'Slc25a19', 'Slc25a22', 'D430042O09Rik', 'D5Ertd579e', 'D630003M21Rik', 'Slc25a29', 'Slc25a3', 'D630039A03Rik', 'D630044L22Rik', 'Slc25a4', 'D830024N08Rik', 'Slc25a47', 'Slc25a5', 'D830039M14Rik', 'D830044D21Rik', 'Slc26a1', 'Slc26a2', 'Slc26a3', 'D930048N14Rik', 'Daam2', 'Dab2', 'Dach1', 'Dact1', 'Slc28a3', 'Dact3', 'Dad1', 'Dagla', 'Dalrd3', 'Dancr', 'Slc2a5', 'Dapk1', 'Dapk2', 'Daw1', 'Dazap2', 'Slc30a4', 'Dbi', 'Dbil5', 'Slc30a8', 'Dbndd2', 'Slc32a1', 'Dbp', 'Dbx2', 'Dcaf17', 'Dcaf6', 'Dcc', 'Dcdc2a', 'Dcdc2b', 'Dclk1', 'Slc36a4', 'Dcn', 'Slc38a1', 'Dctn3', 'Slc38a9', 'Slc39a1', 'Slc39a12', 'Dcx', 'Slc3a2', 'Slc40a1', 'Ddhd1', 'Slc41a1', 'Slc41a2', 'Ddias', 'Ddit4', 'Ddn', 'Ddo', 'Ddr1', 'Slc44a4', 'Slc45a3', 'Ddx17', 'Slc46a3', 'Slc4a10', 'Ddx3y', 'Slc4a4', 'Ddx43', 'Ddx5', 'Slc5a1', 'Slc5a11', 'Slc5a3', 'Slc5a7', 'Dedd2', 'Slc6a1', 'Slc6a11', 'Slc6a13', 'Slc6a15', 'Defb39', 'Defb40', 'Degs1', 'Slc6a20a', 'Slc6a2', 'Dennd1a', 'Dennd1b', 'Slc6a7', 'Slc6a9', 'Slc7a10', 'Slc7a11', 'Slc7a2', 'Slc7a3', 'Depdc5', 'Deptor', 'Derl3', 'Desi2', 'Slc9a3r1', 'Dgat2', 'Dgkb', 'Slc9a9', 'Slc9b1', 'Dgkg', 'Dgki', 'Slco1c1', 'Slco2a1', 'Slco2b1', 'Dhcr24', 'Dhcr7', 'Slf1', 'Dhrs1', 'Dhrs3', 'Slit2', 'Slitrk2', 'Slitrk3', 'Slitrk4', 'Slmap', 'Slpi', 'Dhx58os', 'Slx1b', 'Diaph2', 'Diaph3', 'Dicer1', 'Dido1', 'Smad3', 'Dio2', 'Dio3os', 'Dip2c', 'Diras2', 'Dis3l2', 'Disc1', 'Dixdc1', 'Dkk3', 'Smco2', 'Dlec1', 'Smdt1', 'Smg1', 'Dlg2', 'Dlg4', 'Smg8', 'Dlgap1', 'Dlgap3', 'Dlk1', 'Dlk2', 'Dll1', 'Dll3', 'Smim18', 'Smim22', 'Dlx2', 'Smim26', 'Dlx4', 'Dmd', 'Dmp1', 'Dmrta1', 'Dmrta2', 'Dmtf1', 'Dmtn', 'Dmxl1', 'Smpdl3a', 'Sms', 'Dnaaf3', 'Smtnl2', 'Dnah11', 'Dnah12', 'Smyd1', 'Smyd3', 'Dnah2', 'Dnah3', 'Snap25', 'Dnah6', 'Dnah7a', 'Dnah7b', 'Dnah14', 'Snap91', 'Dnah9', 'Dnaic1', 'Sncb', 'Snca', 'Dnaja4', 'Dnajb13', 'Snhg11', 'Dnajb9', 'Dnajc1', 'Snhg20', 'Snhg3', 'Snhg4', 'Snhg8', 'Snhg9', 'Dnajc27', 'Snrk', 'Dnajc30', 'Snrnp200', 'Dnajc6', 'Dnal1', 'Snrpg', 'Snrpn', 'Dnd1', 'Dnm1', 'Sntg1', 'Dnm3', 'Sntn', 'Snu13', 'Dnmt3a', 'Dnmt3c', 'Doc2a', 'Snx20', 'Dock1', 'Dock10', 'Dock11', 'Snx25', 'Dock3', 'Dock4', 'Dock5', 'Snx29', 'Snx3', 'Snx30', 'Dok6', 'Dok7', 'Sobp', 'Dolpp1', 'Dop1a', 'Sod3', 'Dpf3', 'Son', 'Sorbs1', 'Sorbs2', 'Sorbs2os', 'Dpm3', 'Sorcs2', 'Sorcs3', 'Dpp10', 'Dpp6', 'Sorl1', 'Sos2', 'Sorcs1', 'Dpp4', 'Dppa2', 'Dppa5a', 'Sowaha', 'Sox13', 'Dpyd', 'Sox2', 'Sox2ot', 'Sox4', 'Sox5', 'Drc1', 'Sox5os4', 'Sox6', 'Sox8', 'Sox9', 'Drd3', 'Dsc2', 'Sp5', 'Spa17', 'Dst', 'Spaca6', 'Spaca3', 'Dthd1', 'Dtna', 'Dtnb', 'Spag16', 'Spag17', 'Dtnbos', 'Dtx1', 'Spag9', 'Dtx3', 'Sparc', 'Sparcl1', 'Dubr', 'Spata17', 'Spata16', 'Spata18', 'Spata19', 'Dusp1', 'Dusp14', 'Dusp26', 'Dusp27', 'Spata7', 'Dusp5', 'Dusp6', 'Dusp7', 'Spatc1l', 'Spc24', 'Spc25', 'Spcs1', 'Spcs2', 'Dydc1', 'Spdef', 'Dync1i1', 'Specc1', 'Specc1l', 'Dync2h1', 'Dynll1', 'Spef2', 'Dynll2', 'Dynlt1b', 'Spg20', 'Sphk1', 'Dytn', 'Dzank1', 'Spin4', 'Dzip1l', 'Spink2', 'E030037K01Rik', 'E030042O20Rik', 'E030044B06Rik', 'E130006D01Rik', 'E130102H24Rik', 'Spns2', 'E130307A14Rik', 'Spock2', 'Spock1', 'Spon1', 'Spock3', 'E230013L22Rik', 'Spred1', 'E230032D23Rik', 'E230034D01Rik', 'Spsb4', 'Sptlc3', 'Sptssa', 'Ebi3', 'Sqle', 'Ece1', 'Ecel1', 'Ech1', 'Echdc2', 'Srcap', 'Srcin1', 'Srd5a3', 'Srebf2', 'Srek1', 'Ecpas', 'Srgap1', 'Srgap3', 'Ect2l', 'Edil3', 'Ednrb', 'Edrf1', 'Srrm3', 'Eef1a1', 'Eef1a2', 'Srsf1', 'Eef1akmt3', 'Eef1akmt4', 'Srsf2', 'Eef1b2', 'Eef1d', 'Eef1g', 'Eef2', 'Ssbp2', 'Ssbp3', 'Ssbp4', 'Efcab10', 'Ssc4d', 'Sspo', 'Efcc1', 'Efemp1', 'Efhb', 'Ssr4', 'Sst', 'Efhd1', 'Efhd2', 'Ssx2ip', 'Efna2', 'Efna5', 'St3gal2', 'St3gal4', 'Efr3b', 'St5', 'Eftud2', 'St6gal2', 'Egfem1', 'Egfl6', 'St6galnac3', 'St6galnac5', 'St6galnac1', 'Egfr', 'Egln3', 'Egr1', 'Egr2', 'Egr3', 'St8sia3', 'St8sia3os', 'St8sia4', 'Ehd2', 'Ehd3', 'Stab2', 'Ehmt1', 'Stag1', 'Eif1', 'Stambpl1', 'Stard13', 'Eif3f', 'Eif3h', 'Stat3', 'Eif4a2', 'Stat4', 'Stau2', 'Stbd1', 'Eif4g3', 'Stc2', 'Eif5a', 'Steap4', 'Stim1', 'Stimate', 'Elavl3', 'Elavl4', 'Eldr', 'Elfn2', 'Stk32a', 'Elmo1', 'Elmo2', 'Stk32c', 'Elmod1', 'Stk39', 'Elob', 'Stmn1', 'Stmn2', 'Elof1', 'Stmn3', 'Elovl2', 'Stom', 'Stmn4', 'Elovl5', 'Elovl6', 'Ston2', 'Elp4', 'Stox2', 'Emb', 'Strbp', 'Emc4', 'Emc7', 'Emc8', 'Stx16', 'Eml2', 'Eml5', 'Eml6', 'Stx1b', 'Emp1', 'Emp3', 'Emsy', 'Emx2', 'Emx2os', 'Stxbp1', 'Stxbp4', 'Enah', 'Stxbp5l', 'Sub1', 'Enho', 'Enkd1', 'Enkur', 'Eno1', 'Eno2', 'Eno4', 'Enoph1', 'Enpep', 'Sulf2', 'Enpp2', 'Enpp1', 'Sult1a1', 'Sult2a6', 'Ensa', 'Enthd1', 'Entpd1', 'Sult4a1', 'Entpd3', 'Sumo2', 'Epb41l1', 'Supt3', 'Epb41l3', 'Epc1', 'Susd4', 'Epcam', 'Epha10', 'Epha3', 'Sv2c', 'Epha5', 'Svil', 'Svop', 'Ephb3', 'Swap70', 'Swi5', 'Swsap1', 'Ephx3', 'Syce2', 'Epn2', 'Sycp2', 'Sycp3', 'Eps15', 'Syn2', 'Eps8', 'Syn3', 'Sync', 'Eps8l1', 'Syne1', 'Syndig1l', 'Syne4', 'Syngap1', 'Erbb4', 'Syngr3', 'Synj1', 'Synj2bp', 'Ercc3', 'Synpo', 'Synpo2', 'Ercc6l2', 'Synrg', 'Syp', 'Ergic3', 'Erh', 'Syt10', 'Sys1', 'Syt1', 'Syt11', 'Syt3', 'Syt4', 'Syt7', 'Sytl2', 'Sytl4', 'Sytl5', 'Erv3', 'T2', 'Tac1', 'Esrrb', 'Esrrg', 'Ess2', 'Tada1', 'Etfb', 'Taf10', 'Taf13', 'Etnppl', 'Taf15', 'Etv4', 'Etv5', 'Taf7l', 'Eva1b', 'Tafa1', 'Tafa3', 'Tafa5', 'Exd1', 'Tagln2', 'Tagln3', 'Taldo1', 'Exoc2', 'Tanc2', 'Exoc3l2', 'Exoc6b', 'Ext1', 'Extl1', 'Extl3', 'Eya1', 'Tasor', 'Eya4', 'Tasp1', 'Ezr', 'Tbata', 'F2', 'Tbc1d10c', 'F3', 'F5', 'F630206G17Rik', 'F730035M05Rik', 'Tbc1d22bos', 'F830208F22Rik', 'F930017D23Rik', 'Tbc1d32', 'Tbc1d5', 'Fabp12', 'Fabp5', 'Fabp7', 'Tbcb', 'Fads2', 'Fads3', 'Tbce', 'Faim2', 'Fam107a', 'Fam110a', 'Tbx18', 'Fam114a1', 'Fam120c', 'Fam122b', 'Tcaf1', 'Fam126b', 'Tcaf2', 'Fam129b', 'Fam135a', 'Tceal9', 'Fam155a', 'Tcf20', 'Tcf4', 'Tcf7l1', 'Fam163a', 'Fam163b', 'Fam166b', 'Fam169b', 'Tcp11', 'Fam171a2', 'Fam177a', 'Tctex1d1', 'Tctex1d4', 'Fam181a', 'Fam181b', 'Fam184a', 'Tdgf1', 'Tdrd3', 'Fam193a', 'Fam193b', 'Tdrd9', 'Tead1', 'Fam20a', 'Fam214a', 'Tecr', 'Fam216b', 'Tef', 'Tek', 'Fam222b', 'Tekt3', 'Fam227b', 'Fam228a', 'Fam228b', 'Fam229a', 'Tenm2', 'Tenm3', 'Tenm4', 'Tenm1', 'Tent5c', 'Fam3c', 'Fam43b', 'Tes3-ps', 'Fam57b', 'Tet1', 'Tet2', 'Tex22', 'Fam83d', 'Fam92b', 'Tex9', 'Fanci', 'Fap', 'Tfec', 'Far1os', 'Farp1', 'Fars2', 'Tgfa', 'Fas', 'Fasn', 'Tgfb1i1', 'Fat3', 'Tgfbr3', 'Fau', 'Tgfbr3l', 'Tgif1', 'Fbll1', 'Fbln1', 'Fbln5', 'Thada', 'Fbn2', 'Fbp1', 'Fbrsl1', 'Fbxl13', 'Fbxl14', 'Fbxl16', 'Fbxl17', 'Thbs4', 'Fbxl20', 'Themis2', 'Fbxo15', 'Thoc1', 'Fbxo2', 'Thoc3', 'Thra', 'Thrb', 'Thrsp', 'Tia1', 'Tiam2', 'Ticam2', 'Fbxo9', 'Fbxw10', 'Fbxw18', 'Fbxw4', 'Fcgr2b', 'Fcgr3', 'Timp1', 'Timp3', 'Timp4', 'Fcrls', 'Fdft1', 'Tjp1', 'Tjp3', 'Tlcd1', 'Fem1a', 'Tle2', 'Fez1', 'Tlnrd1', 'Fgd2', 'Fgd4', 'Fgd6', 'Fgf1', 'Fgf10', 'Fgf12', 'Fgf13', 'Fgf14', 'Fgf20', 'Tma7', 'Fgf5', 'Tmbim6', 'Fgf9', 'Tmc2', 'Fgfr1', 'Fgfr3', 'Tmc7', 'Fgfr4', 'Tmcc1', 'Fggy', 'Tmcc2', 'Tmcc3', 'Tmco1', 'Fgl2', 'Fhad1', 'Fhitos', 'Fhl1', 'Fhl2', 'Tmeff1', 'Tmeff2', 'Fibp', 'Tmem100', 'Ficd', 'Filip1l', 'Firre', 'Fis1', 'Tmem117', 'Fjx1', 'Tmem123', 'Fkbp1a', 'Tmem125', 'Fkbp3', 'Tmem130', 'Tmem132b', 'Tmem132cos', 'Tmem140', 'Flnc', 'Flrt1', 'Flrt2', 'Flt3l', 'Flvcr1', 'Tmem151a', 'Fmc1', 'Fmn2', 'Tmem161b', 'Fmo1', 'Tmem164', 'Fmod', 'Fn3krp', 'Fnbp1', 'Tmem173', 'Fnbp4', 'Tmem176a', 'Tmem176b', 'Tmem178b', 'Tmem179', 'Fndc3b', 'Fndc5', 'Tmem181a', 'Tmem191c', 'Folr1', 'Folr2', 'Fos', 'Fosb', 'Fosl2', 'Foxa1', 'Foxa3', 'Tmem202', 'Foxc1', 'Tmem204', 'Foxd1', 'Foxd2os', 'Tmem208', 'Foxg1', 'Tmem212', 'Tmem203', 'Tmem216', 'Foxl2', 'Foxn3', 'Foxo1', 'Tmem229a', 'Foxo4', 'Foxp1', 'Tmem232', 'Tmem245', 'Frat2', 'Frem1', 'Frmd4a', 'Tmem256', 'Tmem258', 'Tmem262', 'Frmpd1', 'Frmpd1os', 'Frmpd2', 'Frmpd3', 'Frmpd4', 'Tmem269', 'Frrs1l', 'Fry', 'Fryl', 'Frzb', 'Tmem35a', 'Tmem37', 'Fscn1', 'Fstl1', 'Tmem44', 'Fstl4', 'Fth1', 'Tmem50a', 'Ftl1', 'Ftx', 'Fuca1', 'Tmem59', 'Tmem59l', 'Fuca2', 'Tmem61', 'Tmem63a', 'Tmem64', 'Fut4', 'Fut8', 'Fut9', 'Fxr1', 'Fxyd1', 'Tmem86a', 'Fxyd5', 'Tmem87a', 'Fxyd7', 'Fxyd6', 'Tmem88', 'Fyn', 'Fzd10', 'Fzd10os', 'Fzd2', 'Fzd3', 'Fzd4', 'Tmem9b', 'Tmem95', 'Tmem98', 'Fzd5', 'G0s2', 'Tmppe', 'Tmprss12', 'G630016G05Rik', 'Tmprss6', 'G630093K05Rik', 'Tmsb10', 'Tmsb15b1', 'Tmsb15b2', 'Tmsb4x', 'Tmtc2', 'Gab2', 'Gabarap', 'Gabarapl2', 'Gabbr1', 'Gabbr2', 'Tmx4', 'Tnc', 'Gabra1', 'Gabra2', 'Gabra3', 'Gabra6', 'Gabrb1', 'Gabrb3', 'Tnfrsf12a', 'Gabrg1', 'Gabrg3', 'Tnfrsf19', 'Gabrq', 'Gabrr2', 'Gad1', 'Tnfrsf26', 'Gad2', 'Gadd45a', 'Gadd45b', 'Gadd45g', 'Tnfsf11', 'Gal', 'Gal3st1', 'Tnfsf12', 'Tnfsf4', 'Tnfsf8', 'Tnik', 'Galnt10', 'Tnk2', 'Galnt16', 'Tnmd', 'Galnt18', 'Galnt2', 'Galnt5', 'Tnni3', 'Tnni3k', 'Galnt9', 'Tnnt3', 'Tnrc18', 'Tnrc6a', 'Tnrc6b', 'Tnrc6c', 'Tns1', 'Gapdh', 'Gap43', 'Garem1', 'Gars', 'Gas1', 'Togaram2', 'Gas2l2', 'Gas5', 'Tom1l1', 'Tom1l2', 'Tomm5', 'Tomm6os', 'Tomm7', 'Gata3', 'Gata4', 'Top1mt', 'Gatc', 'Top2a', 'Gatd3a', 'Tor1b', 'Gbf1', 'Tox', 'Tox2', 'Gbp2', 'Gbp3', 'Tpcn1', 'Gbp6', 'Tpd52', 'Tpd52l1', 'Gbx2', 'Gc', 'Gch1', 'Tpm3-rs7', 'Gcnt1', 'Tppp', 'Gcnt4', 'Gcnt7', 'Tppp3', 'Gda', 'Gdap1', 'Tprn', 'Gdf10', 'Tpt1', 'Trac', 'Gdpd3', 'Tradd', 'Traf1', 'Gem', 'Gfap', 'Gfm1', 'Traj27', 'Traj33', 'Gfra1', 'Trank1', 'Trappc2l', 'Ggnbp1', 'Gh', 'Trcg1', 'Ghr', 'Trdn', 'Gigyf1', 'Trhde', 'Gimap9', 'Triap1', 'Trib2', 'Tril', 'Gins4', 'Trim17', 'Trim2', 'Gja1', 'Gjb1', 'Gjb2', 'Gjb6', 'Gjc2', 'Gjc3', 'Gk', 'Trim34b', 'Gldnos', 'Gle1', 'Glg1', 'Gli1', 'Gli2', 'Gli3', 'Trim47', 'Trim59', 'Glipr1l3', 'Glipr2', 'Glis1', 'Glis3', 'Glmp', 'Trim67', 'Glp2r', 'Trim8', 'Trim9', 'Glrb', 'Trip11', 'Trip12', 'Trip6', 'Glt8d1', 'Glt8d2', 'Glul', 'Glycam1', 'Gnai1', 'Trmt61a', 'Trmt9b', 'Gnao1', 'Gnaq', 'Gnas', 'Trmu', 'Tro', 'Gnb2', 'Gnb5', 'Trp63', 'Gng13', 'Trpa1', 'Gng3', 'Gng5', 'Trpm1', 'Trpm3', 'Gnmt', 'Gnpda2', 'Gnpnat1', 'Trpm7', 'Trps1', 'Trpv2', 'Golga4', 'Tsc22d1', 'Tsc22d3', 'Tsc22d4', 'Got1', 'Got2', 'Tshr', 'Gpam', 'Tshz2', 'Tsix', 'Gpatch2', 'Tsnaxip1', 'Gpatch4', 'Gpc3', 'Tspan15', 'Gpc5', 'Gpc6', 'Gpcpd1', 'Tspan17', 'Gpd2', 'Gphn', 'Tspan5', 'Tspan4', 'Gpld1', 'Gpm6a', 'Tspan7', 'Tspan9', 'Gpm6b', 'Gpnmb', 'Tspoap1', 'Tspyl3', 'Tspyl5', 'Gpr141', 'Tst', 'Gpr146', 'Gpr143', 'Gpr153', 'Gpr155', 'Gpr158', 'Ttc12', 'Gpr160', 'Gpr162', 'Ttc17', 'Gpr161', 'Ttc21a', 'Gpr17', 'Gpr176', 'Ttc23', 'Gpr18', 'Ttc28', 'Ttc29', 'Ttc3', 'Gpr27', 'Gpr37', 'Gpr37l1', 'Ttc34', 'Ttc36', 'Gpr55', 'Gpr6', 'Ttc39d', 'Ttc7b', 'Gpr83', 'Gpr84', 'Ttc9', 'Gpr87', 'Ttc9b', 'Gprasp1', 'Gprasp2', 'Gprc5a', 'Gprc5b', 'Ttll1', 'Gprc5c', 'Ttll10', 'Gprin1', 'Ttll3', 'Ttll4', 'Ttll5', 'Ttll6', 'Gpt2', 'Gpx1', 'Ttll8', 'Gprin2', 'Gpx8', 'Gramd1b', 'Ttyh1', 'Gramd3', 'Gramd4', 'Tuba1a', 'Tuba1b', 'Grasp', 'Grb10', 'Tuba1c', 'Grcc10', 'Tubb2a', 'Tubb2b', 'Tubb3', 'Tubal3', 'Tubb4b', 'Tubb5', 'Tubb6', 'Gria1', 'Gria2', 'Gria3', 'Gria4', 'Grid1', 'Grid2', 'Tubg2', 'Grid2ip', 'Grifin', 'Grik2', 'Grik3', 'Grik4', 'Grik5', 'Tusc1', 'Grin2b', 'Grin2c', 'Tut4', 'Grin3a', 'Grin3b', 'Grip1', 'Grip2', 'Grk3', 'Grk4', 'Grm3', 'Grm4', 'Grm7', 'Grrp1', 'Gsdmd', 'Gsdme', 'Gse1', 'U2surp', 'Uaca', 'Uap1', 'Gsg1l2', 'Gsk3b', 'Uba52', 'Gsn', 'Uba7', 'Gssos1', 'Gsta1', 'Gsta4', 'Ubash3b', 'Gstm1', 'Gstm2', 'Gstm5', 'Ube2e2', 'Gsto1', 'Gstp1', 'Gstt2', 'Ube2l6', 'Gstz1', 'Ube2t', 'Ube3c', 'Gtf2ird1', 'Ube4bos1', 'Ubfd1', 'Gtpbp2', 'Ubr1', 'Ubr3', 'Ubqln4', 'Ubr5', 'Gtsf1', 'Ubtd1', 'Ubxn1', 'Gucy1a2', 'Ubxn11', 'Guk1', 'Uchl1', 'Gulp1', 'Gyg', 'Uckl1os', 'Ucma', 'Gypc', 'Gzmc', 'Ucp3', 'H1f0', 'Uggt2', 'Ugt1a-all', 'H2-K1', 'Uhrf1', 'Ulbp1', 'Ulk4', 'Umad1', 'H2-T23', 'H2afj', 'Unc13c', 'H2afz', 'H3f3a', 'H3f3b', 'Unc45bos', 'Haao', 'Habp4', 'Unc5b', 'Unc5cl', 'Unc80', 'Uncx', 'Hadhb', 'Hal', 'Upf1', 'Hao2', 'Hap1', 'Hapln1', 'Upp2', 'Hapln4', 'Uqcc2', 'Uqcc3', 'Uqcr10', 'Uqcr11', 'Uqcrb', 'Uqcrc1', 'Uqcrfs1', 'Uqcrh', 'Uqcrq', 'Urgcp', 'Haus8', 'Hccs', 'Ush2a', 'Hcfc1r1', 'Usp15', 'Hdac11', 'Hdac4', 'Hdac9', 'Usp29', 'Hdc', 'Usp34', 'Hdhd5', 'Usp45', 'Usp46os2', 'Hebp2', 'Hectd4', 'Usp53', 'Usp54', 'Heg1', 'Usp6nl', 'Hemk1', 'Hepacam', 'Utrn', 'Herc1', 'Herc2', 'Uty', 'Uvssa', 'Herpud1', 'Hes1', 'Hes5', 'Vamp1', 'Hesx1', 'Hexa', 'Heyl', 'Vash1', 'Vasn', 'Vasp', 'Vat1', 'Vat1l', 'Hgh1', 'Vav3', 'Vcl', 'Hif3a', 'Higd1a', 'Higd1b', 'Higd1c', 'Vegfa', 'Hint1', 'Hip1', 'Hipk2', 'Vill', 'Vim', 'Vip', 'Hist1h1t', 'Hist1h2ad', 'Hist1h2bb', 'Hist1h2bc', 'Vmn2r15', 'Vmn2r18', 'Vmn2r28', 'Vmn2r29', 'Vmn2r30', 'Hist1h3g', 'Hist1h4j', 'Hist1h4m', 'Hist2h2aa1', 'Hist2h2bb', 'Vps13a', 'Vps13b', 'Vps13c', 'Vps13d', 'Hivep3', 'Hivep2', 'Hk2', 'Hlf', 'Hmcn1', 'Hmga1b', 'Vps54', 'Hmgb1', 'Hmgb2', 'Hmgcr', 'Hmgcs1', 'Hmgcs2', 'Hmgn1', 'Hmgn2', 'Vsnl1', 'Vsig4', 'Hmgn5', 'Vstm2l', 'Vstm4', 'Hmox1', 'Vti1a', 'Hnf4aos', 'Vtn', 'Vwa3a', 'Vwa3b', 'Hnrnpa2b1', 'Hnrnpf', 'Vwa8', 'Vwa7', 'Wars2', 'Wasf3', 'Washc2', 'Hook3', 'Hopx', 'Hoxa1', 'Hpcal1', 'Hpcal4', 'Wdfy3', 'Wdpcp', 'Hprt', 'Wdr1', 'Hpn', 'Wdr17', 'Hpse', 'Hpse2', 'Hr', 'Hras', 'Wdr33', 'Hrh1', 'Hrh4', 'Wdr45', 'Wdr49', 'Wdr63', 'Hsbp1', 'Wdr66', 'Wdr7', 'Hsd11b1', 'Wdr72', 'Wdr76', 'Wdr78', 'Wdr81', 'Wdr86', 'Hsd17b6', 'Wdr93', 'Wdr95', 'Hsf3', 'Wfdc15a', 'Hsp90aa1', 'Hsp90ab1', 'Hspa12a', 'Wfdc6a', 'Wfikkn1', 'Wfikkn2', 'Whamm', 'Hspa2', 'Wipf1', 'Hspa8', 'Hspb1', 'Wls', 'Wnk1', 'Wnk2', 'Hspb6', 'Hspb8', 'Hspb3', 'Wnt2', 'Hspe1', 'Hspg2', 'Wnt6', 'Wnt7a', 'Htr1f', 'Wnt9a', 'Htr2b', 'Wsb1', 'Htr5a', 'Htr6', 'Htra1', 'Wwc1', 'Wwox', 'Hus1b', 'Hydin', 'Hyou1', 'Xist', 'Hypk', 'Xkr6', 'Xkr9', 'Icam1', 'Icosl', 'Id1', 'Id2', 'Id3', 'Id4', 'Xpr1', 'Ide', 'Xrn1', 'Idi1', 'Xylt1', 'Ier2', 'Ier3', 'Ier3ip1', 'Ybx1', 'Yif1a', 'Ifi27', 'Ifi27l2a', 'Ifi47', 'Ifit2', 'Ypel3', 'Ypel4', 'Ifitm2', 'Ywhab', 'Ywhag', 'Ywhah', 'Ywhaz', 'Yy2', 'Zap70', 'Zbbx', 'Zbtb16', 'Ift81', 'Ift88', 'Zbtb20', 'Igdcc3', 'Igf1r', 'Igf2', 'Zbtb3', 'Igf2bp2', 'Igf2bp3', 'Igfbp2', 'Igfbp7', 'Zbtb7c', 'Igfbpl1', 'Zbtb8b', 'Igsf1', 'Igsf10', 'Igsf21', 'Zc3h12b', 'Igsf8', 'Zc3hav1', 'Zcchc12', 'Il10rb', 'Zcwpw2', 'Il15', 'Il16', 'Il17c', 'Il18', 'Zeb1', 'Il1r1', 'Il1rapl1', 'Il1rapl2', 'Zfand2a', 'Zfas1', 'Il20rb', 'Il21r', 'Il22ra1', 'Zfhx3', 'Zfhx4', 'Il31ra', 'Il33', 'Zfp110', 'Il6', 'Il6ra', 'Ildr2', 'Zfp14', 'Zfp157', 'Zfp160', 'Zfp185', 'Impact', 'Impdh2', 'Zfp217', 'Zfp263', 'Inhbc', 'Inka1', 'Ino80', 'Ino80d', 'Ino80dos', 'Zfp296', 'Inpp4b', 'Zfp319', 'Zfp329', 'Zfp330', 'Zfp335', 'Insig1', 'Zfp335os', 'Insl3-Jak3', 'Insl6', 'Insr', 'Zfp36', 'Insyn2a', 'Insyn2b', 'Zfp365', 'Zfp385a', 'Zfp398', 'Zfp407', 'Zfp414', 'Iqca', 'Iqcc', 'Iqcd', 'Zfp423', 'Iqch', 'Zfp451', 'Iqsec1', 'Iqsec3', 'Iqub', 'Zfp462', 'Irak2', 'Irak4', 'Zfp474', 'Irf2', 'Zfp503', 'Zfp513', 'Zfp516', 'Irf7', 'Zfp521', 'Zfp524', 'Irs2', 'Zfp532', 'Zfp536', 'Irx3', 'Irx5', 'Zfp575', 'Iscu', 'Zfp592', 'Ism1', 'Zfp608', 'Isyna1', 'Zfp629', 'Itga6', 'Zfp638', 'Zfp641', 'Zfp644', 'Itgam', 'Zfp655', 'Itgb2', 'Itgbl1', 'Itih3', 'Zfp692', 'Itm2b', 'Itm2c', 'Zfp708', 'Itpka', 'Itpkb', 'Itpr1', 'Itpr2', 'Itsn2', 'Ivd', 'Izumo4', 'Zfp772', 'Zfp773', 'Jag1', 'Jak3', 'Jakmip1', 'Zfp799', 'Jaml', 'Jarid2', 'Zfp808', 'Jazf1', 'Zfp811', 'Jhy', 'Zfp827', 'Jph2', 'Jph3', 'Jph4', 'Jpt1', 'Zfp85', 'Jpx', 'Zfp853', 'Jun', 'Junb', 'Kalrn', 'Kank1', 'Kansl1', 'Kansl1l', 'Kantr', 'Kat2a', 'Zfp936', 'Kat6b', 'Kat8', 'Katnal2', 'Kbtbd12', 'Zfp950', 'Kcna2', 'Kcna6', 'Kcnab1', 'Kcnab3', 'Kcnb2', 'Kcnc1', 'Kcnc4', 'Kcnd2', 'Zfp981', 'Kcng1', 'Kcng2', 'Kcng3', 'Kcng4', 'Zfp991', 'Kcnh3', 'Zfpm2', 'Kcnip1', 'Kcnip3', 'Kcnip4', 'Kcnj10', 'Kcnj11', 'Kcnj16', 'Zhx2', 'Kcnj3', 'Zic1', 'Zic2', 'Kcnj2', 'Zic4', 'Kcnj4', 'Kcnk1', 'Zic3', 'Zim3', 'Kcnk3', 'Kcnk5', 'Kcnma1', 'Kcnmb1', 'Kcnn2', 'Kcnn3', 'Zmynd10', 'Kcnq1', 'Kcnq1ot1', 'Kcnn4', 'Kcnq3', 'Kcns2', 'Kcns3', 'Kcnt1', 'Znrf3', 'Kcnt2', 'Kctd12b', 'Kctd17', 'Zscan26', 'Zswim5', 'Zswim6', 'Zup1', 'Zwint', 'Zzef1', 'a', 'Kdm5d', 'Kdm8', 'Khdrbs3', 'Kidins220', 'Kif13a', 'Kif13b', 'Kif19a', 'Kif1b', 'Kif22', 'Kif23', 'Kif27', 'Kif2c', 'Kif3c', 'Kif5a', 'Kif5c', 'Kif6', 'Kif9', 'Kifc5b', 'Kirrel2', 'Kirrel3', 'Kiss1', 'Kit', 'Kiz', 'Klb', 'Klc1', 'Klc2', 'Klc3', 'Klf10', 'Klf12', 'Klf2', 'Klf4', 'Klf5', 'Klf6', 'Klf7', 'Klf9', 'Klhdc10', 'Klhdc7a', 'Klhl10', 'Klhl25', 'Klhl29', 'Klhl32', 'Klra8', 'Klre1', 'Klri1', 'Kmt2a', 'Kmt2c', 'Kmt2e', 'Kmt5b', 'Kpna2', 'Krt19', 'Krt25', 'Krt8', 'Krtcap2', 'Ktn1', 'Ky', 'L1cam', 'L3hypdh', 'L3mbtl4', 'Lad1', 'Lama2', 'Lamb1', 'Lamb3', 'Lamp1', 'Lamtor2', 'Lao1', 'Laptm4a', 'Large1', 'Larp1', 'Lars2', 'Layn', 'Lbp', 'Lca5l', 'Lck', 'Lcorl', 'Lctl', 'Ldb2', 'Ldc1', 'Ldha', 'Ldhb', 'Ldhc', 'Ldlr', 'Ldlrad4', 'Lenep', 'Leng8', 'Leprot', 'Lfng', 'Lgals1', 'Lgals12', 'Lgals2', 'Lgals3', 'Lgi1', 'Lgi3', 'Lgmn', 'Lgr6', 'Lgsn', 'Lhfp', 'Lhfpl2', 'Lhfpl3', 'Lhx2', 'Lhx5', 'Limch1', 'Limk2', 'Lingo2', 'Lipm', 'Lipo3', 'Lix1', 'Lmntd1', 'Lmo3', 'Lmod1', 'Lncbate6', 'Lncpint', 'Lnpep', 'Lonrf2', 'Lor', 'Loxhd1', 'Loxl2', 'Lpar1', 'Lpar3', 'Lpgat1', 'Lpin2'])

subregional_model = joblib.load(
    f"models/svm/best-logit-subregional_astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle",
)
subregional_model

/opt/python/3.8.8/lib/python3.8/site-packages/sklearn/base.py:318: UserWarning:

Trying to unpickle estimator RobustScaler from version 1.2.1 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations

/opt/python/3.8.8/lib/python3.8/site-packages/sklearn/base.py:318: UserWarning:

Trying to unpickle estimator LogisticRegression from version 1.2.1 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations

/opt/python/3.8.8/lib/python3.8/site-packages/sklearn/base.py:318: UserWarning:

Trying to unpickle estimator SelectFromModel from version 1.2.1 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations

/opt/python/3.8.8/lib/python3.8/site-packages/sklearn/base.py:318: UserWarning:

Trying to unpickle estimator SVC from version 1.2.1 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations

/opt/python/3.8.8/lib/python3.8/site-packages/sklearn/base.py:318: UserWarning:

Trying to unpickle estimator Pipeline from version 1.2.1 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations

Pipeline(steps=[('scaling', RobustScaler()),
                ('feature_selection',
                 SelectFromModel(estimator=LogisticRegression(max_iter=10000,
                                                              multi_class='ovr',
                                                              n_jobs=-1,
                                                              penalty='l1',
                                                              random_state=42,
                                                              solver='saga'),
                                 max_features=1406, threshold=-inf)),
                ('classify', SVC(C=10, max_iter=10000))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

print(uni_fs[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist())

['0610005C13Rik', '0610043K17Rik', '1190005I06Rik', 'Lrp8os2', '1500004A13Rik', '1500015L24Rik', '1600010M07Rik', '1600012H06Rik', 'Lrrc45', 'Lrrc57', '1700003D09Rik', '1700003G18Rik', '1700003M07Rik', '1700008C04Rik', 'Lrrd1', 'Lrrk1', 'Lsm8', '1700022H01Rik', '1700024B05Rik', 'Ltbp1', '1700025F22Rik', '1700027I24Rik', '1700028E10Rik', '1700029I15Rik', '1700030F04Rik', 'Ly86', '1700030L22Rik', '1700030N03Rik', '1700034I23Rik', 'Lysmd4', 'M1ap', '1700054A03Rik', '1700060O08Rik', '1700061G19Rik', '1700064H15Rik', 'Mageb18', '1700081N11Rik', '1700084C06Rik', '1700095A13Rik', '1700095J03Rik', 'Mamdc2', 'Mamdc4', 'Maml3', '1700102P08Rik', '1700108F19Rik', 'Mannr', 'Maob', 'Map1b', '1700125G22Rik', 'Map2k4', 'Map3k11', '1810020O05Rik', '1810030O07Rik', 'Map4k3', 'Map7', '2010007H06Rik', '2210016F16Rik', '2310030A07Rik', '2310030G06Rik', '2310039L15Rik', 'Masp1', '2310068J16Rik', '2510009E07Rik', '2610001J05Rik', '2600006K01Rik', 'Mboat4', '2610307P16Rik', 'Mccc1os', '2610318N02Rik', '2700033N17Rik', 'Mcm6', '2810030D12Rik', '2810410L24Rik', 'Mcur1', 'Mdfi', '2810459M11Rik', '2900052N01Rik', 'Mdp1', '3110021N24Rik', '3110039M20Rik', '3110056K07Rik', '3200001D21Rik', '3222401L13Rik', 'Mef2c', 'Meg3', '4731419I09Rik', 'Meikin', 'Meis2', '4732496C06Rik', '4833422C13Rik', 'Metap1d', '4833423E24Rik', 'Mettl11b', 'Mettl17', '4921507P07Rik', '4921511C10Rik', 'Mettl24', '4921539H07Rik', '4930404I05Rik', '4930404N11Rik', 'Mfng', 'Mfsd14b', 'Mfsd2a', 'Mfsd3', '4930415C11Rik', '4930415O20Rik', 'Mfsd7a', 'Mgam', 'Mgam2-ps', '4930432B10Rik', '4930439D14Rik', '4930441H08Rik', '4930445B16Rik', '4930447C04Rik', 'Mid1', '4930451E10Rik', 'Miip', 'Mill2', '4930455B14Rik', '4930467E23Rik', '4930470G03Rik', 'Mir124-2hg', '4930473A02Rik', 'Mlh3', '4930512B01Rik', 'Mmp11', 'Mmrn2', '4930529K09Rik', 'Mogat1', '4930545L23Rik', 'Mok', '4930549C15Rik', 'Morn2', '4930554G24Rik', '4930554H23Rik', '4930555B11Rik', 'Morn5', '4930556G01Rik', '4930556J24Rik', 'Mpg', 'Mpped1', 'Mpped2', '4930589L23Rik', 'Mrgpra6', '4930594M22Rik.1', '4931403E22Rik', 'Mroh7', '4931413K12Rik', '4932411K12Rik', '4932416K20Rik', '4932435O22Rik', '4932443L11Rik', 'Mrpl42', '4933403O08Rik', '4933406F09Rik', 'Mrps18b', '4933408J17Rik', 'Msantd2', 'Msh6', '4933431E20Rik', 'Msl3', '4933433H22Rik', 'Mta1', 'Mta3', '5031434O11Rik', 'Mtcp1', '5033426O07Rik', 'Mtfp1', 'Mtg1', '5430416N02Rik', 'Mthfs', '5430431A17Rik', 'Mtmr4', '5730522E02Rik', '5830444B04Rik', 'Mttp', 'Mtus1', '5930435M05Rik', '6030498E09Rik', '6330403K07Rik', 'Mus81', 'Mvd', '6430503K07Rik', '6330576A10Rik', '6430553K19Rik', '6430628N08Rik', '6530402F18Rik', '6530409C15Rik', '6530411M01Rik', '6720427I07Rik', 'Mycbp', 'Mycl', '8030453O22Rik', 'Myh6', 'Mylip', '9130008F23Rik', '9130017K11Rik', '9130221H12Rik', 'Myo1f', 'Myoc', 'Myorg', '9330158H04Rik', 'Myrfl', 'Myrip', '9330199G10Rik', 'Naa10', '9630001P10Rik', '9630014M24Rik', 'Naip1', '9630028H03Rik', 'Nalcn', 'Napb', 'A1bg', 'A230020J21Rik', 'A230051N06Rik', 'Nat8f7', 'Naxe', 'A230108P19Rik', 'A330076C08Rik', 'A330087D11Rik', 'A330094K24Rik', 'A4galt', 'Ncoa6', 'Ncor2', 'A530053G22Rik', 'Ndnf', 'A630001O12Rik', 'A630012P03Rik', 'A630023A22Rik', 'A730020M07Rik', 'A730049H05Rik', 'Ndufaf4', 'A730063M14Rik', 'A830035A12Rik', 'Necab3', 'Necap1', 'A930002C04Rik', 'Nectin1', 'A930006K02Rik', 'Nefh', 'Nek11', 'Nek8', 'AA465934', 'AB124611', 'Nell2', 'AC113595.1', 'AC163638.2', 'AI429214', 'AI463229', 'Nfic', 'AI849053', 'Nfkbia', 'Nfkbib', 'Nfkbil1', 'AU023762', 'AW146154', 'Nhs', 'AY036118', 'Nifk', 'Aarsd1', 'Aasdhppt', 'Aass', 'Aatk', 'Nkain2', 'Nkd1', 'Nkx2-1', 'Nkx2-2', 'Nkx2-2os', 'Nkx2-4', 'Abcb1a', 'Abcb9', 'Nlgn2', 'Abcc10', 'Abcc2', 'Abcf2', 'Nme3', 'Abhd15', 'Nmnat2', 'Nmur1', 'Abl1', 'Ablim2', 'Acap3', 'Notum', 'Acat2', 'Acat3', 'Ackr3', 'Ackr4', 'Acp1', 'Acsbg2', 'Nr1d1', 'Nr1d2', 'Nr2f2', 'Actr3b', 'Acvr1c', 'Acy3', 'Adam11', 'Nrg2', 'Adam5', 'Adamts12', 'Nsd2', 'Nsdhl', 'Nsf', 'Adamts2', 'Nsmce1', 'Nsmce3', 'Adat2', 'Ntn1', 'Ntn3', 'Ntng1', 'Nuak2', 'Adgrb2', 'Adgrf4', 'Adgrg2', 'Adgrl2', 'Nudt16l1', 'Adra1b', 'Adrb1', 'Nupr1l', 'Aff3', 'Afm', 'Agap2', 'Agbl1', 'Nxpe5', 'Oacyl', 'Ahctf1', 'Ahdc1', 'Ahsa1', 'Ogdhl', 'Ogfod2', 'Ogfod3', 'Ajm1', 'Olfml2b', 'Olfr1511', 'Akr1b3', 'Olfr455', 'Akt2', 'Olfr653', 'Olfr655', 'Olfr656', 'Olfr66', 'Olig2', 'Omg', 'Onecut2', 'Opa1', 'Alkal2', 'Alkbh7', 'Alpl', 'Alyref2', 'Amdhd2', 'Amer2', 'Ammecr1', 'Amot', 'Otoa', 'Amy1', 'Angel1', 'Otx1', 'Otx2', 'Otx2os1', 'Ovca2', 'Angptl4', 'Ankfn1', 'P2rx3', 'P2ry12', 'P2ry13', 'P2ry6', 'Ankrd22', 'P3h4', 'Pacsin1', 'Pacsin2', 'Padi2', 'Paf1', 'Ankzf1', 'Anln', 'Pank1', 'Paox', 'Anxa9', 'Aox1', 'Pard3bos1', 'Ap5s1', 'Apc2', 'Pawr', 'Pax5', 'Apln', 'Apmap', 'Apobec4', 'Aqp5', 'Pcdhb2', 'Aqp9', 'Ar', 'Pcdhb22', 'Pcgf1', 'Pck2', 'Arg2', 'Arhgap23', 'Pcsk2', 'Pcsk2os1', 'Arhgap31', 'Arhgap33', 'Arhgap40', 'Arhgap6', 'Pdcd1lg2', 'Arhgef10', 'Pde10a', 'Arhgef18', 'Arhgef28', 'Arhgef9', 'Arid3a', 'Pdgfb', 'Arl13a', 'Arl4d', 'Pdlim1', 'Armc2', 'Armcx4', 'Arntl', 'Pdzph1', 'Arpp21', 'Peak1os', 'Pebp4', 'Peg3', 'Arsj', 'Per2', 'Arx', 'Arvcf', 'Asb2', 'Pfkfb4', 'Ascl4', 'Asic1', 'Asic4', 'Asna1', 'Pglyrp1', 'Phactr2', 'Phf21b', 'Atic', 'Phldb2', 'Atoh7', 'Pi15', 'Atp23', 'Atp6v1e2', 'Atxn2l', 'Pitpnm3', 'Pitpnm2os2', 'Atxn7l2', 'Pitx2', 'Atxn7l3', 'Piwil4', 'Pkd1l2', 'Pkdcc', 'Pkhd1', 'Pkib', 'B130024G19Rik', 'Pla2g10', 'Pla2r1', 'Plat', 'Platr27', 'Platr8', 'Plbd1', 'B430010I23Rik', 'Plcb3', 'Pld5', 'B830042I05Rik', 'Plek2', 'Plekha6', 'BC017158', 'BC034090', 'Plekho2', 'BC037032', 'Plin3', 'Plk2', 'BC050972', 'BC051226', 'Plppr5', 'Bach2os', 'Plut', 'Bag3', 'Bahcc1', 'Baiap2', 'Baiap3', 'Bambi', 'Plxnc1', 'Pmepa1', 'Barx2', 'Pmepa1os', 'Bbox1', 'Bbs1', 'Bcam', 'Bcar3', 'Pnpla2', 'Pnrc1', 'Bcas3os1', 'Podnl1', 'Pogk', 'Poglut1', 'Poglut2', 'Pold2', 'Bcl2l2', 'Polr1c', 'Becn1', 'Begain', 'Bet1l', 'Bicd2', 'Pou3f4', 'Bloc1s2', 'Bmp5', 'Bmp7', 'Bmx', 'Bnip2', 'Boc', 'Ppip5k2', 'Ppp1r12c', 'Ppp1r13b', 'Brd1', 'Ppp1r15a', 'Ppp1r16b', 'Brd3os', 'Ppp1r1b', 'Ppp1r26', 'Brip1os', 'Ppp1r3a', 'Ppp1r3d', 'Ppp1r9b', 'Ppp2r2c', 'Bsx', 'Btbd11', 'Btbd3', 'Btg2', 'Bves', 'Prag1', 'Prcd', 'C030007H22Rik', 'Prdm1', 'C030018K13Rik', 'C030032O16Rik', 'C030034L19Rik', 'Prdm8', 'Prdx4', 'Prelp', 'C1galt1c1', 'Prickle1', 'Prkaa2', 'C1s1', 'Prkag2os1', 'C2', 'Prkcb', 'Prkcd', 'C230034O21Rik', 'Prkcz', 'C2cd4c', 'C2cd4d', 'C330002G04Rik', 'C330013E15Rik', 'C4b', 'C530025M09Rik', 'C5ar1', 'C5ar2', 'Prox2', 'C8g', 'Prpf31', 'Cab39l', 'Prr11', 'Prrc2b', 'Cacna1h', 'Cacna2d2', 'Prrt1', 'Prrt2', 'Prss27', 'Prss36', 'Prss41', 'Prss45', 'Caln1', 'Calml4', 'Camk2a', 'Camk2b', 'Psme2b', 'Capsl', 'Ptch2', 'Ptchd4', 'Car15', 'Car3', 'Car8', 'Ptgs1', 'Pth1r', 'Carmil2', 'Casp6', 'Ptpn7', 'Catsperb', 'Ptprh', 'Catspere1', 'Ptpro', 'Cbfa2t3', 'Purg', 'Pygm', 'Ccdc134', 'Ccdc152', 'Ccdc162', 'Rab11fip4', 'Ccdc171', 'Rab20', 'Rab26os', 'Ccdc22', 'Ccdc24', 'Rab33b', 'Ccdc34os', 'Ccdc38', 'Ccdc84', 'Ccdc85c', 'Ccdc86', 'Rad21l', 'Ccn1', 'Ralgapb', 'Ccn6', 'Ralyl', 'Rap1gap', 'Rapgef5', 'Cd164l2', 'Rasgrf1', 'Rasl11a', 'Rasl11b', 'Rassf2', 'Raver2', 'Rax', 'Rbis', 'Cd82', 'Rbm42', 'Cd96', 'Rbms2', 'Rbms3', 'Rbmxl1', 'Cdc42ep1', 'Rbpms', 'Cdc42ep4', 'Rbsn', 'Rcor2', 'Rcsd1', 'Cdh23', 'Recql5os1', 'Cdk10', 'Rfxap', 'Cebpb', 'Rgl2', 'Rgs2', 'Cenpt', 'Rhcg', 'Rhd', 'Rhobtb1', 'Rhobtb2', 'Ribc2', 'Rif1', 'Rimbp2', 'Ces5a', 'Ripor2', 'Cfap100', 'Cfap161', 'Rmi1', 'Rmnd5a', 'Cfap46', 'Rnd1', 'Cfap69', 'Rnf121', 'Cfap77', 'Rnf122', 'Cfb', 'Cfp', 'Rnf152', 'Rnf24', 'Rnf5', 'Chil1', 'Robo2', 'Rogdi', 'Ror1', 'Ror2', 'Chrac1', 'Chrdl1', 'Rpe65', 'Chst3', 'Ciart', 'Cib1', 'Cic', 'Rrp15', 'Rsf1os1', 'Rsf1os2', 'Rslcan18', 'Cklf', 'Ckmt2', 'Rspo2', 'Rtl4', 'Clcn6', 'Rubie', 'Rufy2', 'Rundc3a', 'Clec3b', 'Clic3', 'Ryr1', 'Clic6', 'S100pbp', 'S1pr5', 'Sall3', 'Samd12', 'Sap30bpos', 'Sap30l', 'Cmtr2', 'Pigm', 'Cndp2', 'Cnnm3', 'Scg5', 'Cntn2', 'Cntnap1', 'Scly', 'Scpep1os', 'Scrib', 'Col1a1', 'Col1a2', 'Scrt1', 'Scube1', 'Scrt2', 'Col25a1', 'Col4a3', 'Sdk2', 'Sds', 'Col9a2', 'Colec12', 'Sec14l5', 'Selenbp1', 'Coq10b', 'Coq6', 'Corin', 'Coro2b', 'Sema3c', 'Sema3b', 'Sema3d', 'Sema4a', 'Sema3f', 'Cox6a2', 'Sema5a', 'Sema5b', 'Sema7a', 'Sept4', 'Cpne6', 'Serpinb5', 'Serpinb9', 'Serpine1', 'Serpine3', 'Cr2', 'Crebzf', 'Creld2', 'Sez6l', 'Sf3b4', 'Sfmbt1', 'Crlf2', 'Sfrp1', 'Crocc', 'Sft2d2', 'Sfxn2', 'Sgcz', 'Crybg2', 'Crym', 'Sgsm1', 'Sh2b1', 'Csmd2', 'Sh3gl2', 'Sh3glb2', 'Cstdc2', 'Sh3rf2', 'Sh3rf3', 'Sh3tc2', 'Shank2', 'Ctif', 'Shh', 'Shisa3', 'Shisa6', 'Shmt1', 'Ctsh', 'Shmt2', 'Shprh', 'Shroom2', 'Sik1', 'Cutal', 'Sim2', 'Sipa1l3', 'Cxcr4', 'Sirt7', 'Six3', 'Six3os1', 'Cyb561', 'Cyb561d1', 'Skida1', 'Cyp2c50', 'Cyp2c55', 'Slc13a3', 'Cyp2c69', 'Slc13a5', 'Slc14a2', 'Slc15a1', 'Slc16a2', 'Cyp4f15', 'Slc17a9', 'Cyp51', 'Cyth1', 'Slc22a12', 'D030046N08Rik', 'Slc22a20', 'D030068K23Rik', 'Slc22a27', 'D130079A08Rik', 'Slc24a4', 'Slc25a19', 'D630003M21Rik', 'Slc25a29', 'Slc25a47', 'Slc26a1', 'Slc26a2', 'Dach1', 'Dancr', 'Slc2a5', 'Dapk1', 'Dapk2', 'Slc30a4', 'Dbil5', 'Dcc', 'Dcdc2a', 'Dcdc2b', 'Slc38a1', 'Slc39a1', 'Ddias', 'Slc45a3', 'Slc46a3', 'Ddx3y', 'Slc5a11', 'Slc5a7', 'Defb39', 'Defb40', 'Slc6a7', 'Slc7a2', 'Slc7a3', 'Deptor', 'Derl3', 'Dgat2', 'Slc9b1', 'Dgki', 'Dhcr24', 'Dhcr7', 'Dhrs3', 'Slitrk2', 'Dhx58os', 'Slx1b', 'Diaph3', 'Dicer1', 'Smad3', 'Dio2', 'Diras2', 'Disc1', 'Dixdc1', 'Dkk3', 'Dlg4', 'Smg8', 'Dlk1', 'Smim22', 'Dlx2', 'Dmp1', 'Dmrta2', 'Smyd1', 'Dnaic1', 'Dnaja4', 'Snhg3', 'Snhg4', 'Snhg9', 'Dnajc27', 'Dnajc30', 'Dnd1', 'Sntg1', 'Doc2a', 'Snx20', 'Dock10', 'Dock11', 'Snx25', 'Dock5', 'Snx30', 'Dok6', 'Dok7', 'Dolpp1', 'Sod3', 'Sorcs3', 'Sos2', 'Sowaha', 'Sox13', 'Sox4', 'Drc1', 'Sp5', 'Dusp1', 'Dusp27', 'Dusp6', 'Spatc1l', 'Specc1l', 'Spef2', 'Dynlt1b', 'Spg20', 'Dzank1', 'Spin4', 'Spink2', 'E030042O20Rik', 'E230032D23Rik', 'E230034D01Rik', 'Spsb4', 'Sqle', 'Ece1', 'Echdc2', 'Srcap', 'Srcin1', 'Srd5a3', 'Edrf1', 'Eef1akmt3', 'Eef1akmt4', 'Ssbp3', 'Ssbp4', 'Efcab10', 'Ssc4d', 'Sspo', 'Efcc1', 'Efhd1', 'Efhd2', 'Efna2', 'St3gal2', 'Eftud2', 'St6gal2', 'Egfem1', 'Egfl6', 'St6galnac3', 'St6galnac5', 'Egln3', 'Egr1', 'St8sia3os', 'Ehd2', 'Ehd3', 'Stab2', 'Stambpl1', 'Stat4', 'Stimate', 'Elavl4', 'Elfn2', 'Stk32a', 'Elmo1', 'Elmo2', 'Elmod1', 'Stk39', 'Elovl2', 'Stom', 'Elovl6', 'Emc4', 'Eml2', 'Stx1b', 'Emsy', 'Emx2', 'Emx2os', 'Enkd1', 'Enkur', 'Sulf2', 'Enpp2', 'Sult1a1', 'Sult2a6', 'Enthd1', 'Entpd3', 'Susd4', 'Epha3', 'Epha5', 'Svil', 'Ephb3', 'Swap70', 'Syn3', 'Eps8l1', 'Syne4', 'Syngap1', 'Synj1', 'Ercc3', 'Ercc6l2', 'Syt3', 'Sytl5', 'Erv3', 'Esrrb', 'Ess2', 'Tada1', 'Taf13', 'Etv4', 'Etv5', 'Taf7l', 'Eva1b', 'Tafa1', 'Exoc2', 'Extl1', 'Eya1', 'Eya4', 'F2', 'Tbc1d10c', 'F5', 'F630206G17Rik', 'Fads3', 'Fam126b', 'Fam163a', 'Fam163b', 'Fam177a', 'Fam181a', 'Tdrd3', 'Fam193b', 'Tek', 'Fam228a', 'Fam229a', 'Fam3c', 'Fam43b', 'Fam57b', 'Tex22', 'Fam92b', 'Far1os', 'Fas', 'Tgfbr3', 'Tgfbr3l', 'Tgif1', 'Fbll1', 'Fbln1', 'Fbln5', 'Fbp1', 'Fbxl14', 'Fbxo15', 'Thoc3', 'Ticam2', 'Fbxo9', 'Fcgr2b', 'Fcgr3', 'Fcrls', 'Fdft1', 'Fem1a', 'Tle2', 'Tlnrd1', 'Fgf10', 'Fgf13', 'Fgf20', 'Fgf5', 'Fgf9', 'Fgfr4', 'Tmcc2', 'Fhad1', 'Fhl1', 'Tmeff1', 'Fibp', 'Tmem100', 'Filip1l', 'Firre', 'Tmem123', 'Tmem125', 'Tmem132b', 'Tmem140', 'Flrt1', 'Flrt2', 'Flt3l', 'Flvcr1', 'Fmo1', 'Fn3krp', 'Tmem173', 'Fnbp4', 'Fndc5', 'Tmem181a', 'Tmem191c', 'Folr1', 'Folr2', 'Fos', 'Foxa1', 'Foxa3', 'Tmem204', 'Foxd1', 'Foxd2os', 'Foxg1', 'Tmem203', 'Tmem216', 'Tmem245', 'Frem1', 'Frmpd1os', 'Frmpd4', 'Tmem269', 'Frzb', 'Tmem37', 'Tmem44', 'Tmem50a', 'Fuca1', 'Fuca2', 'Tmem63a', 'Fut4', 'Tmem86a', 'Fzd10', 'Fzd10os', 'Fzd2', 'Fzd4', 'G0s2', 'G630016G05Rik', 'Tmprss6', 'G630093K05Rik', 'Gabra2', 'Gabra6', 'Tnfrsf19', 'Gabrr2', 'Tnfrsf26', 'Tnfsf11', 'Tnfsf12', 'Tnfsf4', 'Tnfsf8', 'Galnt10', 'Tnk2', 'Galnt2', 'Galnt5', 'Tnni3', 'Tns1', 'Gas1', 'Togaram2', 'Gas2l2', 'Tomm6os', 'Gata4', 'Gatc', 'Gatd3a', 'Tor1b', 'Tpd52l1', 'Gc', 'Gcnt1', 'Gcnt4', 'Gcnt7', 'Gdf10', 'Trac', 'Gdpd3', 'Gfm1', 'Traj27', 'Traj33', 'Ggnbp1', 'Gh', 'Trcg1', 'Trdn', 'Gigyf1', 'Triap1', 'Gins4', 'Gjb2', 'Gjc2', 'Gjc3', 'Trim34b', 'Gldnos', 'Gle1', 'Gli1', 'Glipr1l3', 'Glipr2', 'Glmp', 'Trim8', 'Glrb', 'Trip6', 'Glt8d2', 'Trmu', 'Trp63', 'Trpm1', 'Gnmt', 'Gnpda2', 'Gnpnat1', 'Trpv2', 'Got2', 'Tshr', 'Tsix', 'Gpatch4', 'Gpc3', 'Tspan4', 'Tspoap1', 'Gpr141', 'Gpr146', 'Gpr153', 'Gpr155', 'Ttc12', 'Gpr162', 'Gpr37', 'Ttc34', 'Gpr87', 'Gprasp2', 'Gprc5c', 'Ttll5', 'Gpt2', 'Gprin2', 'Gpx8', 'Gramd4', 'Tubal3', 'Grid2ip', 'Grifin', 'Grik5', 'Grin3a', 'Grin3b', 'Grm4', 'Uap1', 'Gssos1', 'Ubash3b', 'Gstt2', 'Gstz1', 'Ube4bos1', 'Ubfd1', 'Ubqln4', 'Ubtd1', 'Ubxn11', 'Gyg', 'Uckl1os', 'Ucma', 'H1f0', 'Uggt2', 'H2-K1', 'Umad1', 'H2-T23', 'Unc13c', 'Unc80', 'Hal', 'Upf1', 'Hapln1', 'Hccs', 'Ush2a', 'Hdac4', 'Hdc', 'Hdhd5', 'Usp46os2', 'Heg1', 'Usp6nl', 'Hemk1', 'Uty', 'Uvssa', 'Herpud1', 'Hesx1', 'Hexa', 'Heyl', 'Vash1', 'Vasn', 'Vasp', 'Hgh1', 'Higd1b', 'Higd1c', 'Vip', 'Hist1h2ad', 'Vmn2r15', 'Vmn2r29', 'Hist1h4j', 'Hist2h2aa1', 'Hmcn1', 'Hmga1b', 'Hmgb2', 'Hmgcr', 'Hmgcs2', 'Vsig4', 'Hmgn5', 'Vstm4', 'Hnf4aos', 'Wars2', 'Hopx', 'Hoxa1', 'Hpcal1', 'Hpse', 'Hpse2', 'Hr', 'Hrh1', 'Wdr45', 'Hsd11b1', 'Wdr72', 'Wdr76', 'Wdr81', 'Hsd17b6', 'Hsf3', 'Hspa12a', 'Whamm', 'Wipf1', 'Wls', 'Wnt6', 'Wnt7a', 'Htr1f', 'Hyou1', 'Icosl', 'Id1', 'Ide', 'Idi1', 'Ier2', 'Yif1a', 'Ifi27', 'Ifi47', 'Yy2', 'Zap70', 'Igdcc3', 'Zbtb3', 'Igf2bp3', 'Igsf21', 'Zc3hav1', 'Il10rb', 'Zcwpw2', 'Il15', 'Il17c', 'Il1rapl2', 'Zfand2a', 'Il22ra1', 'Il31ra', 'Il33', 'Zfp110', 'Il6ra', 'Zfp157', 'Zfp160', 'Impdh2', 'Zfp217', 'Zfp263', 'Inhbc', 'Inka1', 'Zfp319', 'Zfp330', 'Zfp335', 'Insig1', 'Zfp335os', 'Insyn2a', 'Zfp385a', 'Zfp414', 'Iqcc', 'Iqch', 'Zfp451', 'Iqub', 'Irak4', 'Zfp513', 'Zfp516', 'Irf7', 'Zfp524', 'Irs2', 'Zfp536', 'Irx3', 'Irx5', 'Zfp592', 'Zfp629', 'Itgbl1', 'Zfp708', 'Itpka', 'Itpr1', 'Izumo4', 'Jag1', 'Zfp799', 'Zfp808', 'Zfp811', 'Jph2', 'Zfp85', 'Jpx', 'Zfp853', 'Jun', 'Junb', 'Kat2a', 'Zfp936', 'Kat8', 'Katnal2', 'Kbtbd12', 'Kcna2', 'Kcnc4', 'Zfp981', 'Kcng1', 'Kcng2', 'Kcng3', 'Kcng4', 'Zfp991', 'Kcnh3', 'Kcnip1', 'Kcnj3', 'Zic1', 'Zic2', 'Zic4', 'Zic3', 'Zim3', 'Kcnk5', 'Kcns3', 'Kcnt1', 'Kctd12b', 'Kctd17', 'Kdm5d', 'Kdm8', 'Khdrbs3', 'Kif13b', 'Kif19a', 'Kif3c', 'Kifc5b', 'Kirrel2', 'Klc1', 'Klc2', 'Klhdc7a', 'Klhl10', 'Klhl25', 'Klra8', 'Klre1', 'Krt8', 'Ky', 'L3hypdh', 'L3mbtl4', 'Lamb3', 'Lao1', 'Lars2', 'Lca5l', 'Lck', 'Lctl', 'Ldhc', 'Ldlr', 'Lenep', 'Leprot', 'Lgals12', 'Lgr6', 'Lgsn', 'Lhfpl2', 'Lhx2', 'Lipo3', 'Lmo3', 'Lmod1', 'Lonrf2', 'Loxhd1', 'Loxl2', 'Lpar1', 'Lpgat1', 'Lpin2']

subregional_model2 = joblib.load(
    f"models/svm/best-xgboost-subregional_astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle",
)
subregional_model2

[22:55:59] WARNING: /opt/conda/conda-bld/work/src/learner.cc:553: 
  If you are loading a serialized model (like pickle in Python, RDS in R) generated by
  older XGBoost, please export the model by calling `Booster.save_model` from that version
  first, then load it back in current version. See:

    https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html

  for more details about differences between saving model and serializing.

Pipeline(steps=[('scaling', RobustScaler()),
                ('feature_selection',
                 SelectFromModel(estimator=XGBClassifier(base_score=None,
                                                         booster=None,
                                                         callbacks=None,
                                                         colsample_bylevel=None,
                                                         colsample_bynode=None,
                                                         colsample_bytree=None,
                                                         early_stopping_rounds=None,
                                                         enable_categorical=False,
                                                         eval_metric=None,
                                                         feature_types=None,
                                                         gamma=None,
                                                         gpu_id=None,
                                                         grow_policy=None,
                                                         importa...
                                                         interaction_constraints=None,
                                                         learning_rate=0.3,
                                                         max_bin=None,
                                                         max_cat_threshold=None,
                                                         max_cat_to_onehot=None,
                                                         max_delta_step=None,
                                                         max_depth=3,
                                                         max_leaves=None,
                                                         min_child_weight=None,
                                                         missing=nan,
                                                         monotone_constraints=None,
                                                         n_estimators=100,
                                                         n_jobs=-1,
                                                         num_parallel_tree=None,
                                                         predictor=None,
                                                         random_state=42, ...),
                                 max_features=211, threshold=-inf)),
                ('classify', SVC(C=200, max_iter=10000))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

print(uni_fs[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist())

['Lrp1b', 'Lrrc8a', 'Ltbp1', 'Macrod1', 'Malat1', 'Map3k5', 'Mbp', 'Mdga2', 'Medag', '4833423E24Rik', 'Mfge8', 'Mfsd2a', 'Mir100hg', 'Mir9-3hg', 'Morn1', 'Mrps6', 'Msi2', 'Mt1', 'Mt2', 'Mt3', '5031439G07Rik', 'Myoc', 'Nap1l5', 'Ncald', 'Ndn', 'Ndufa3', 'Ndufb1-ps', 'Nebl', 'Nefl', 'AC149090.1', 'Nhs', 'AY036118', 'Nnat', 'Acot4', 'Nr2f2', 'Nr4a1', 'Nrarp', 'Nrgn', 'Nrp1', 'Nrsn2', 'Nrxn3', 'Adcy8', 'Nupr1', 'Agt', 'Aldoc', 'Ank3', 'Anln', 'Aplp1', 'Apoe', 'App', 'Aqp4', 'Pcsk1n', 'Pde10a', 'Arhgef28', 'Pde4d', 'Pdgfd', 'Pdzph1', 'Arpp21', 'Peg3', 'Atp1a2', 'Atp1b2', 'Atp5g1', 'Atp5k', 'Atp6v0c', 'Atpif1', 'Pitx2', 'Avp', 'Pkib', 'Pla2g10', 'B230323A14Rik', 'Basp1', 'Bc1', 'Ppia', 'Brip1os', 'Bst1', 'Prdx1', 'Prkce', 'Camk2a', 'Camk2n1', 'Ptprd', 'Pygm', 'Ralyl', 'Cd24a', 'Cd81', 'Cd9', 'Resp18', 'Cebpb', 'Rnf165', 'Chchd10', 'Ckb', 'Rtn1', 'Rubie', 'Rundc3a', 'S100a10', 'Cmss1', 'Sap30bpos', 'Scd2', 'Cntn1', 'Cops9', 'Selenop', 'Sema3c', 'Cox4i1', 'Cox6c', 'Cox7a2', 'Cox8a', 'Cpe', 'Sept4', 'Sept7', 'Sfrp1', 'Sgip1', 'Crym', 'Cst3', 'Ctnna2', 'Shisa6', 'Six3', 'Cycs', 'Cyfip2', 'Slc17a6', 'Dbi', 'Dbp', 'Dcc', 'Dcn', 'Slc6a11', 'Slco2a1', 'Slit2', 'Dlg2', 'Dnah12', 'Snhg3', 'Dock10', 'Sorbs1', 'Dpp10', 'Sparcl1', 'Dync1i1', 'Dynll1', 'Spock1', 'Spock3', 'Eef1g', 'Ssbp4', 'Efna5', 'Egfem1', 'Egr1', 'Eif1', 'Elfn2', 'Eno1', 'Sulf2', 'Swi5', 'Fabp5', 'Fabp7', 'Tenm1', 'Far1os', 'Fbln5', 'Thrsp', 'Timp3', 'Fgfr3', 'Tmem173', 'Fos', 'Fosb', 'Foxg1', 'Tmem262', 'Fth1', 'Tmsb4x', 'Gabarap', 'Gadd45g', 'Tnik', 'Gas5', 'Gfap', 'Gh', 'Tril', 'Gnas', 'Trpm3', 'Tsc22d3', 'Tsix', 'Gpc5', 'Gpc6', 'Tspan7', 'Ttc3', 'Tubb2a', 'Gria1', 'Gria2', 'Grin2b', 'Uba52', 'Gstm1', 'Uckl1os', 'H3f3b', 'Uqcrq', 'Usp29', 'Hes5', 'Vim', 'Hmgcs1', 'Vsnl1', 'Hopx', 'Hspa8', 'Xist', 'Id2', 'Ywhab', 'Ywhaz', 'Zbbx', 'Igf2', 'Impact', 'Irak2', 'Itih3', 'Jaml', 'Jun', 'Kcnip4', 'Zic1', 'Lars2', 'Ldhb', 'Lgals3', 'Lgi1', 'Lhfp', 'Lix1']

Evaluate model on test parts of the datasets

PRJNA847050_etest = PRJNA847050_counts[PRJNA847050_counts.obs["test"]]
PRJNA815819_etest = PRJNA815819_counts[PRJNA815819_counts.obs["test"]]
PRJNA798401_etest = PRJNA798401_counts[PRJNA798401_counts.obs["test"]]
PRJNA723345_etest = PRJNA723345_counts[PRJNA723345_counts.obs["test"]]
PRJNA722418_etest = PRJNA722418_counts[PRJNA722418_counts.obs["test"]]
PRJNA705596_etest = PRJNA705596_counts[PRJNA705596_counts.obs["test"]]
PRJNA679294_etest = PRJNA679294_counts[PRJNA679294_counts.obs["test"]]
PRJNA611624_etest = PRJNA611624_counts[PRJNA611624_counts.obs["test"]]
PRJNA604055_etest = PRJNA604055_counts[PRJNA604055_counts.obs["test"]]
PRJNA548532_etest = PRJNA548532_counts[PRJNA548532_counts.obs["test"]]
PRJNA515063_etest = PRJNA515063_counts[PRJNA515063_counts.obs["test"]]
PRJNA453138_etest = PRJNA453138_counts[PRJNA453138_counts.obs["test"]]

adata_eval = concat(
    [
        PRJNA847050_etest,
        PRJNA815819_etest,
        PRJNA798401_etest,
        PRJNA722418_etest,
        PRJNA705596_etest,
        PRJNA679294_etest,
        PRJNA611624_etest,
        PRJNA604055_etest,
        PRJNA548532_etest,
        PRJNA515063_etest,
        PRJNA453138_etest,
    ],
    join="outer",
)

sc.pp.normalize_total(adata_eval, target_sum=1e4)
sc.pp.log1p(adata_eval)
sc.pp.regress_out(adata_eval, ["nCount_RNA", "percent_mito_ribo"])

y_eval = adata_eval.obs["region"]
df_eval = pd.DataFrame(
    adata_eval.X, columns=adata_eval.var_names, index=adata_eval.obs_names
)

normalizing counts per cell

    finished (0:00:00)

regressing out ['nCount_RNA', 'percent_mito_ribo']

    sparse input is densified and may lead to high memory use

    finished (0:10:06)

df_eval = df_eval.loc[
    :, uni_fs
]

df_eval.head()

	0610005C13Rik	Lpl	Lpp	Lrat	Lrba	Lrch3	0610043K17Rik	Lrguk	Lrig1	1110008P14Rik	...	Lncpint	Lnpep	Lonrf2	Lor	Loxhd1	Loxl2	Lpar1	Lpar3	Lpgat1	Lpin2
SRR19578591_AAACCCAAGAAACCCG-1	-0.028538	-0.011898	0.689952	-0.004907	0.256085	1.061024	-0.154006	-0.031577	1.325920	-0.095952	...	1.013336	0.455391	-0.206781	-0.005036	-0.000359	-0.009339	-0.070833	-0.002627	-0.077567	-0.414678
SRR19578591_AAACCCATCGCTAATG-1	-0.016152	-0.004665	-0.619428	-0.003315	1.936711	1.339365	-0.068376	-0.019622	1.414424	-0.088126	...	1.339398	-0.316552	-0.158118	-0.010933	-0.000074	-0.005192	-0.051823	-0.001972	-0.066080	-0.230684
SRR19578591_AAACGCTAGCTAGAGC-1	-0.031664	-0.012830	-1.191776	-0.005555	0.111413	-0.790940	-0.174328	-0.035273	0.751429	-0.089438	...	1.426316	-0.552869	0.689715	-0.003167	-0.000417	-0.010424	-0.075356	-0.002937	-0.078118	-0.456309
SRR19578591_AAACGCTGTTGCATGT-1	-0.035506	-0.022803	0.332578	-0.003920	0.959985	0.396472	1.143516	-0.033107	0.591462	-0.165281	...	0.051032	-0.020533	0.374900	-0.004635	-0.000628	-0.011382	0.589117	-0.001894	-0.101989	-0.554878
SRR19578591_AAACGCTTCGCGAAGA-1	-0.036226	-0.031610	0.292413	-0.001702	0.532359	-1.047932	-0.229050	-0.027425	0.026115	-0.245400	...	0.617137	-0.227220	-0.378808	-0.007870	-0.000778	-0.011266	-0.087301	-0.000580	0.439112	0.783025

5 rows × 4217 columns

# Compute the proportion of each category
prop_eval = y_eval.value_counts(normalize=True)

# Sort the categories by their proportion
cat_eval = pd.Categorical(y_eval, categories=prop_eval.index.sort_values(), ordered=True)

# Get the integer codes of the categories
codes_eval = cat_eval.codes

adata_eval.obs["codes_eval"] = adata_eval.obs["region"]
adata_eval.obs["codes_eval"] = pd.Categorical(codes_eval, ordered=True)

Logistic regression based feature selection

predictions_logit_eval = subregional_model.predict(df_eval)

print("Matthews correlation coefficient: {}".format(matthews_corrcoef(codes_eval, predictions_logit_eval)))
print("F-measure: {}".format(f1_score(codes_eval, predictions_logit_eval, average="weighted")))

Matthews correlation coefficient: 0.4408352445523123
F-measure: 0.6273951088822771

adata_eval.obs["predictions_logit_eval"] = adata_eval.obs["region"]
adata_eval.obs["predictions_logit_eval"] = predictions_logit_eval
adata_eval.obs["predictions_logit_eval"] = pd.Categorical(
    values=adata_eval.obs["predictions_logit_eval"].astype(str),
    categories=natsorted(
        map(str, np.unique(adata_eval.obs["predictions_logit_eval"].astype(str)))
    ),
)

reducer_eval = UMAP(
    n_neighbors=ks,
    n_epochs=1000,
    init="spectral",
    learning_rate=0.1,
    min_dist=min_dist,
    spread=spread,
    repulsion_strength=2.0,
    negative_sample_rate=negative_sample_rate,
    densmap=True,
    n_components=2,
    random_state=reseed,
    verbose=False,
)
reducer_eval.fit(
    adata_eval.obsm["X_pca"], adata_eval.obs["predictions_logit_eval"].astype(str)
)
adata_eval.obsm["X_umap_logit"] = reducer_eval.transform(adata_eval.obsm["X_pca"])

save_PRJNA779749 = f"-replic2-best-subregional_supervised-logit-evaluation-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pdf"

sc.pl.embedding(
    adata_eval,
    basis="X_pacmap",
    color=["predictions_logit_eval", "codes_eval", "region"],
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save=save_PRJNA779749,
)

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_pacmap-replic2-best-subregional_supervised-logit-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.embedding(
    adata_eval,
    basis="X_umap_logit",
    color=["predictions_logit_eval"],
    title=f"UMAP: supervised predictions, PRJNA779749 \n(amb.FPR={cb_fpr}, k={k}, MSP={connectivity_model}, \nmetric={metric}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-predict_subregion" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-predict_subregion-replic2-best-subregional_supervised-logit-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.embedding(
    adata_eval,
    basis="X_umap_logit",
    color=["region"],
    title=f"UMAP: supervised subregion, PRJNA779749 \n(amb.FPR={cb_fpr}, k={k}, MSP={connectivity_model}, \nmetric={metric}, n_genes_excluded={signature})",
    add_outline=True,
    legend_loc="on data",
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-region" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-region-replic2-best-subregional_supervised-logit-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.embedding(
    adata_eval,
    basis="X_umap_logit",
    color=["codes_eval"],
    title=f"UMAP: supervised subregion-codes, PRJNA779749 \n(amb.FPR={cb_fpr}, k={k}, MSP={connectivity_model}, \nmetric={metric}, n_genes_excluded={signature})",
    add_outline=True,
    legend_loc="on data",
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-region" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-region-replic2-best-subregional_supervised-logit-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

XGBoost based feature selection

predictions_xgboost_eval = subregional_model2.predict(df_eval)
print("Matthews correlation coefficient: {}".format(matthews_corrcoef(codes_eval, predictions_xgboost_eval)))
print("F-measure: {}".format(f1_score(codes_eval, predictions_xgboost_eval, average="weighted")))

Matthews correlation coefficient: 0.5776601427855099
F-measure: 0.723413495875078

adata_eval.obs["predictions_xgboost_eval"] = adata_eval.obs["region"]
adata_eval.obs["predictions_xgboost_eval"] = predictions_xgboost_eval
adata_eval.obs["predictions_xgboost_eval"] = pd.Categorical(
    values=adata_eval.obs["predictions_xgboost_eval"].astype(str),
    categories=natsorted(
        map(str, np.unique(adata_eval.obs["predictions_xgboost_eval"].astype(str)))
    ),
)

reducer_eval2 = UMAP(
    n_neighbors=ks,
    n_epochs=1000,
    init="spectral",
    learning_rate=0.1,
    min_dist=min_dist,
    spread=spread,
    repulsion_strength=2.0,
    negative_sample_rate=negative_sample_rate,
    densmap=True,
    n_components=2,
    random_state=reseed,
    verbose=False,
)
reducer_eval2.fit(
    adata_eval.obsm["X_pca"], adata_eval.obs["predictions_xgboost_eval"].astype(str)
)
adata_eval.obsm["X_umap"] = reducer_eval2.transform(adata_eval.obsm["X_pca"])

save_PRJNA779749 = f"-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pdf"

sc.pl.embedding(
    adata_eval,
    basis="X_pacmap",
    color=["predictions_xgboost_eval", "codes_eval", "region"],
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save=save_PRJNA779749,
)

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_pacmap-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.umap(
    adata_eval,
    color=["predictions_xgboost_eval"],
    title=f"UMAP: supervised predictions, PRJNA779749 \n(amb.FPR={cb_fpr}, k={k}, MSP={connectivity_model}, \nmetric={metric}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-predict_subregion" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-predict_subregion-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.umap(
    adata_eval,
    color=["region"],
    title=f"UMAP: supervised subregion, PRJNA779749 \n(amb.FPR={cb_fpr}, k={k}, MSP={connectivity_model}, \nmetric={metric}, n_genes_excluded={signature})",
    add_outline=True,
    legend_loc="on data",
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-region" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-region-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.umap(
    adata_eval,
    color=["codes_eval"],
    title=f"UMAP: supervised subregion-codes, PRJNA779749 \n(amb.FPR={cb_fpr}, k={k}, MSP={connectivity_model}, \nmetric={metric}, n_genes_excluded={signature})",
    add_outline=True,
    legend_loc="on data",
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-region" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-region-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

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No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

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adata_eval.write(
    f"/data/data/resolved_subregions_by_microclusters/replic2-best-subregional_supervised-evaluation-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)
pd.DataFrame(adata_eval.obsm["X_umap_logit"], index=adata_eval.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-logit.tsv",
    sep="\t",
    header=True,
)
pd.DataFrame(adata_eval.obsm["X_pacmap"], index=adata_eval.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-pacmap-subfunct.tsv",
    sep="\t",
    header=True,
)
pd.DataFrame(adata_eval.obsm["X_umap"], index=adata_eval.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/replic2-best-subregional_supervised-evaluation-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-xgboost.tsv",
    sep="\t",
    header=True,
)

Plot genes on the evaluation test set

nprdf = pd.DataFrame(adata_eval.raw.X.todense(), columns=adata_eval.var_names, index=adata_eval.obs_names).loc[:, np.array([g for g in npr if g in adata_eval.var_names])]
nprdf.describe()

	Adcyap1r1	Avpr1a	Calcr	Calcrl	Cckar	Cckbr	Cntfr	Crhr1	Crhr2	Esr1	...	Sstr1	Sstr2	Sstr3	Tacr1	Tacr3	Trhr	Trhr2	Tshr	Vipr1	Vipr2
count	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	...	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000
mean	0.813226	0.001375	0.005162	0.005247	0.001010	0.004938	0.518938	0.001263	0.001403	0.013046	...	0.005667	0.004798	0.002722	0.005275	0.004994	0.004714	0.000281	0.061444	0.000505	0.001768
std	1.167632	0.038538	0.105281	0.082063	0.032637	0.072072	0.820081	0.037807	0.038171	0.167247	...	0.078003	0.071886	0.053164	0.093414	0.076230	0.082606	0.016748	0.388574	0.022467	0.043321
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	23.000000	2.000000	10.000000	5.000000	2.000000	2.000000	9.000000	3.000000	2.000000	9.000000	...	2.000000	2.000000	2.000000	7.000000	5.000000	5.000000	1.000000	9.000000	1.000000	2.000000

8 rows × 68 columns

nprdf.quantile(q=[0.5, 0.9, 0.985, 1.0], axis=0)

	Adcyap1r1	Avpr1a	Calcr	Calcrl	Cckar	Cckbr	Cntfr	Crhr1	Crhr2	Esr1	...	Sstr1	Sstr2	Sstr3	Tacr1	Tacr3	Trhr	Trhr2	Tshr	Vipr1	Vipr2
0.500	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.900	2.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.985	4.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
1.000	23.0	2.0	10.0	5.0	2.0	2.0	9.0	3.0	2.0	9.0	...	2.0	2.0	2.0	7.0	5.0	5.0	1.0	9.0	1.0	2.0

4 rows × 68 columns

nprcols = [col for col in nprdf.columns if nprdf[col].quantile(0.985) > 1]
nprcols3 = [col for col in nprdf.columns if col not in nprcols and nprdf[col].max() == 1]
nprcols2 = [col for col in nprdf.columns if col not in nprcols and col not in nprcols3]
print(nprcols)
print(nprcols2)
print(nprcols3)

['Adcyap1r1', 'Cntfr', 'Ghr', 'Igf1r', 'Insr', 'Ntrk2', 'Ntsr2']
['Avpr1a', 'Calcr', 'Calcrl', 'Cckar', 'Cckbr', 'Crhr1', 'Crhr2', 'Esr1', 'Galr1', 'Galr2', 'Ghrhr', 'Ghsr', 'Glp1r', 'Gpr55', 'Gpr83', 'Gpr149', 'Grpr', 'Hcrtr1', 'Hcrtr2', 'Kiss1r', 'Lepr', 'Mc3r', 'Mc4r', 'Mchr1', 'Nmbr', 'Nmur1', 'Nmur2', 'Npffr1', 'Npffr2', 'Npr1', 'Npr2', 'Npr3', 'Npsr1', 'Npy1r', 'Npy2r', 'Npy5r', 'Ntsr1', 'Oprk1', 'Oprl1', 'Oprm1', 'Oxtr', 'Prlhr', 'Prlr', 'Prokr2', 'Qrfpr', 'Rxfp1', 'Rxfp2', 'Sstr1', 'Sstr2', 'Sstr3', 'Tacr1', 'Tacr3', 'Trhr', 'Tshr', 'Vipr2']
['Galr3', 'Insrr', 'Mc1r', 'Oprd1', 'Trhr2', 'Vipr1']

sc.pl.umap(
    adata_eval,
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval,
    color=nprcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-npr2" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr2-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval,
    color=nprcols3,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    na_color="#9A9FB080",
    save="-npr3" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr3-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

nmrdf = pd.DataFrame(adata_eval.raw.X.todense(), columns=adata_eval.var_names, index=adata_eval.obs_names).loc[:, np.array([g for g in nmr if g in adata_eval.var_names])]
nmrdf.describe()

	Adra1a	Adra1b	Adra1d	Adra2a	Adra2b	Adra2c	Adrb1	Adrb2	Adrb3	Adrm1	...	P2rx7	P2ry1	P2ry2	P2ry6	P2ry12	P2ry13	P2ry14	Ryr1	Ryr2	Ryr3
count	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	...	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000
mean	0.125835	0.017143	0.000028	0.166770	0.000140	0.004966	0.064755	0.001038	0.000898	0.165423	...	0.089557	0.034342	0.001431	0.001122	0.044105	0.001206	0.175467	0.002048	0.032939	0.019247
std	0.417641	0.142971	0.005297	0.456052	0.011843	0.071090	0.274346	0.033063	0.029951	0.427272	...	0.315874	0.194620	0.037801	0.035908	0.237281	0.039265	0.502083	0.045827	0.336484	0.248945
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	8.000000	6.000000	1.000000	8.000000	1.000000	2.000000	5.000000	2.000000	1.000000	5.000000	...	4.000000	3.000000	1.000000	2.000000	10.000000	3.000000	10.000000	2.000000	24.000000	12.000000

8 rows × 141 columns

nmrcols = [col for col in nmrdf.columns if nmrdf[col].quantile(0.985) > 1]
nmrcols3 = [col for col in nmrdf.columns if col not in nmrcols and nmrdf[col].max() == 1]
nmrcols2 = [col for col in nmrdf.columns if col not in nmrcols and col not in nmrcols3]
print(nmrcols)
print(nmrcols2)
print(nmrcols3)

['Adra1a', 'Adra2a', 'Adrm1', 'Adora1', 'Adora2b', 'Gria1', 'Gria2', 'Gria4', 'Grid1', 'Grid2', 'Grik2', 'Grik4', 'Grik5', 'Grin2b', 'Grin2c', 'Grin3a', 'Grm5', 'Grm3', 'Grm7', 'Gabra2', 'Gabra4', 'Gabrb1', 'Gabrb3', 'Gabrg1', 'Gabrg3', 'Gabbr1', 'Gabbr2', 'Gnas', 'Gnai2', 'Gnai3', 'Gnao1', 'Gnaq', 'Gna11', 'Gna12', 'Gna13', 'Gnb1', 'Gnb2', 'Gng3', 'Gng5', 'Gng10', 'Gng11', 'Gng12', 'P2ry14']
['Adra1b', 'Adra2c', 'Adrb1', 'Adrb2', 'Adora2a', 'Chrm1', 'Chrm2', 'Chrm3', 'Chrm5', 'Chrna2', 'Chrna3', 'Chrna4', 'Chrna5', 'Chrna7', 'Chrna9', 'Chrnb1', 'Chrnb2', 'Chrnd', 'Chrng', 'Gria3', 'Grik1', 'Grik3', 'Grin1', 'Grin2a', 'Grin2d', 'Grin3b', 'Grm1', 'Grm4', 'Grm6', 'Grm8', 'Gabra1', 'Gabra3', 'Gabra5', 'Gabra6', 'Gabrb2', 'Gabrg2', 'Gabrd', 'Gabre', 'Gabrq', 'Gabrr2', 'Drd1', 'Drd2', 'Drd3', 'Htr1a', 'Htr1b', 'Htr1d', 'Htr1f', 'Htr2a', 'Htr2b', 'Htr2c', 'Htr3b', 'Htr4', 'Htr5a', 'Htr6', 'Htr7', 'Gnai1', 'Gnal', 'Gnb4', 'Gnb5', 'Gng2', 'Gng4', 'Gng7', 'Gng13', 'Gngt2', 'P2rx3', 'P2rx4', 'P2rx5', 'P2rx6', 'P2rx7', 'P2ry1', 'P2ry6', 'P2ry12', 'P2ry13', 'Ryr1', 'Ryr2', 'Ryr3']
['Adra1d', 'Adra2b', 'Adrb3', 'Adora3', 'Chrm4', 'Chrna1', 'Chrna6', 'Chrna10', 'Chrnb3', 'Grm2', 'Gabrp', 'Gabrr1', 'Gabrr3', 'Drd4', 'Drd5', 'Htr3a', 'Htr5b', 'Gnb3', 'Gng8', 'P2rx1', 'P2rx2', 'P2ry2']

sc.pl.umap(
    adata_eval,
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval,
    color=nmrcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-nmr2" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr2-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval,
    color=nmrcols3,
    frameon=False,
    show=True,
    size=40,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    vmin=0.0,
    na_color="#9A9FB080",
    save="-nmr3" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr3-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

adddf = pd.DataFrame(adata_eval.raw.X.todense(), columns=adata_eval.var_names, index=adata_eval.obs_names).loc[:, np.array([g for g in genes_embed if g in adata_eval.var_names])]
adddf.describe()

	Abcd1	Abcd2	Abcd3	Acaa2	Acox1	Agt	Aldh1a1	Aldh1l1	Aldoc	Apoe	...	Slc4a4	Slc6a11	Slc7a10	Slc38a1	Slit1	Slit2	Sox9	Tafa1	Tkt	Trpv1
count	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	...	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000
mean	0.043460	0.355816	0.378346	0.301302	0.318220	8.957186	0.624264	0.524297	9.349953	73.621651	...	9.552943	8.631474	2.253100	0.309186	0.022530	0.806492	1.158549	0.234134	0.765221	0.000224
std	0.213308	0.695445	0.699256	0.611500	0.653968	8.292150	1.388164	0.892931	9.212760	62.865395	...	9.160531	8.108604	2.283527	1.087161	0.188686	2.424775	1.660665	0.937980	1.064716	0.014980
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	2.000000	25.000000	...	3.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	7.000000	0.000000	0.000000	7.000000	62.000000	...	7.000000	6.000000	2.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000
75%	0.000000	1.000000	1.000000	0.000000	0.000000	13.000000	1.000000	1.000000	14.000000	104.000000	...	13.000000	12.000000	3.000000	0.000000	0.000000	1.000000	2.000000	0.000000	1.000000	0.000000
max	3.000000	11.000000	8.000000	7.000000	9.000000	77.000000	46.000000	15.000000	103.000000	1206.000000	...	177.000000	87.000000	20.000000	23.000000	7.000000	68.000000	27.000000	55.000000	10.000000	1.000000

8 rows × 78 columns

agcols = [col for col in adddf.columns if adddf[col].quantile(0.985) > 1]
agcols3 = [col for col in adddf.columns if col not in agcols and adddf[col].max() == 1]
agcols2 = [col for col in adddf.columns if col not in agcols and col not in agcols3]
print(agcols)
print(agcols2)
print(agcols3)

['Abcd2', 'Abcd3', 'Acaa2', 'Acox1', 'Agt', 'Aldh1a1', 'Aldh1l1', 'Aldoc', 'Apoe', 'Aqp4', 'Ckb', 'Cst3', 'Dcc', 'Ech1', 'Egfr', 'Eno1', 'Fgfr3', 'Fis1', 'Fos', 'Fth1', 'Ftl1', 'Gfap', 'Gja1', 'Gli1', 'Glul', 'Hacd2', 'Hadhb', 'Hepacam', 'Htra1', 'Lcat', 'Lpcat3', 'Lxn', 'Mfn1', 'Mgll', 'Ndrg2', 'Nfia', 'Opa1', 'Pex13', 'Pex14', 'Pex2', 'Pex6', 'Pla2g7', 'Plcb1', 'Pygb', 'S100a6', 'S100b', 'Scd2', 'Sgcd', 'Slc1a2', 'Slc1a3', 'Slc4a4', 'Slc6a11', 'Slc7a10', 'Slc38a1', 'Slit2', 'Sox9', 'Tafa1', 'Tkt']
['Abcd1', 'Cnr1', 'Cnr2', 'Dagla', 'Daglb', 'Decr2', 'Dnm1', 'Faah', 'Mfn2', 'Mief1', 'Napepld', 'Otp', 'Pex1', 'Pex10', 'Pex12', 'Pex16', 'Pex26', 'Pex3', 'Slit1']
['Trpv1']

sc.pl.umap(
    adata_eval,
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval,
    color=agcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-adgen2" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen2-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval,
    color=agcols3,
    frameon=False,
    show=True,
    size=40,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    vmin=0.0,
    na_color="#9A9FB080",
    save="-adgen3" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen3-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

tfdf = pd.DataFrame(adata_eval.raw.X.todense(), columns=adata_eval.var_names, index=adata_eval.obs_names).loc[:, np.array([g for g in tf_genes if g in adata_eval.var_names])]
tfdf.describe()

	Aff3	Alx1	Arid1a	Arid1b	Arid2	Arid3a	Arid3b	Arid3c	Arid4a	Arid4b	...	Caskin1	Gtf3c4	Gcdh	BC024139	Zfp398	Tcfl5	A630089N07Rik	Phf8	Zhx3	Zfp82
count	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.0	35642.000000	35642.000000	...	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000	35642.000000
mean	0.183856	0.000281	0.339010	0.400847	0.332950	0.004349	0.024381	0.0	0.266876	0.404158	...	0.402194	0.044077	0.090062	0.002665	0.163347	0.000224	0.118512	0.132877	0.730795	0.014954
std	0.523519	0.016748	0.646764	0.755183	0.682789	0.067901	0.160123	0.0	0.592014	0.735323	...	0.710927	0.214101	0.307537	0.052101	0.441810	0.014980	0.395160	0.394780	1.123800	0.125464
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.000000	1.000000	1.000000	1.000000	0.000000	0.000000	0.0	0.000000	1.000000	...	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000
max	9.000000	1.000000	7.000000	11.000000	21.000000	2.000000	2.000000	0.0	11.000000	11.000000	...	7.000000	3.000000	4.000000	2.000000	6.000000	1.000000	6.000000	6.000000	16.000000	3.000000

8 rows × 1417 columns

tfcols = [col for col in tfdf.columns if tfdf[col].quantile(0.9) > 3]
tfcols3 = [col for col in tfdf.columns if col not in tfcols and tfdf[col].max() == 1]
tfcols2 = [col for col in tfdf.columns if col not in tfcols and tfdf[col].max() > 1]

print(tfcols)
print(tfcols2)
print(tfcols3)

['Ddx5', 'Nkx6-2', 'Rora', 'Rorb', 'Sox5', 'Sox6', 'Son', 'Hdac8', 'Id2', 'Npas3', 'Nfia', 'Ptch1', 'Pbx1', 'Syne1', 'Tcf4', 'Trps1', 'Zbtb20', 'Nfib', 'Ank2']
['Aff3', 'Arid1a', 'Arid1b', 'Arid2', 'Arid3a', 'Arid3b', 'Arid4a', 'Arid4b', 'Arid5b', 'Bcl3', 'Bcl6', 'Bdp1', 'Btaf1', 'Bag1', 'Bcor', 'Brf1', 'Bach1', 'Bach2', 'Barx2', 'Bmi1', 'Ctbp1', 'Ctbp2', 'C1d', 'Cebpa', 'Cebpb', 'Cebpd', 'Cebpg', 'Cebpz', 'Ctcf', 'Cnot3', 'Cops2', 'Crebbp', 'Cited1', 'Cited2', 'Cited4', 'Dbp', 'Dpf2', 'Ddx3x', 'Ddit3', 'Drap1', 'Ep300', 'Ets1', 'Ets2', 'E2f1', 'E2f2', 'E2f3', 'E2f4', 'E2f5', 'E2f6', 'E2f7', 'E4f1', 'Elf1', 'Elf2', 'Elf4', 'Elf5', 'Elk1', 'Elk3', 'Elk4', 'Eaf2', 'Eid1', 'Eid2', 'Erf', 'Ewsr1', 'Fosb', 'Fos', 'Daxx', 'Fezf2', 'Fli1', 'Gabpa', 'Gata2', 'Gata4', 'Gatad2b', 'Gli1', 'Gli2', 'Gli3', 'Glis2', 'Gsx1', 'Hmx2', 'Herc1', 'Hnf1b', 'Ikzf1', 'Ikzf3', 'Isl1', 'Irx3', 'Jdp2', 'Kat2a', 'Kat2b', 'Kat5', 'Kat6a', 'Klf1', 'Klf10', 'Klf12', 'Klf13', 'Klf15', 'Klf16', 'Klf4', 'Klf5', 'Klf7', 'Klf9', 'Kcnip3', 'Lpp', 'Lhx1', 'Lhx2', 'Lhx3', 'Lhx4', 'Lmx1b', 'Mxd1', 'Mxi1', 'Mlx', 'Mecom', 'Mybbp1a', 'Mycbp', 'Maz', 'Max', 'Meis1', 'Meis2', 'Mdfi', 'Nkx2-1', 'Nkx6-1', 'Nanog', 'Nab2', 'Paxip1', 'Pou2f1', 'Pou2f2', 'Pou3f1', 'Pou3f2', 'Pou3f4', 'Pou4f1', 'Pou6f1', 'Patz1', 'Prdm1', 'Pknox2', 'Pknox1', 'Rab11fip3', 'Rbak', 'Rest', 'Rcor2', 'Rybp', 'Rrn3', 'Arhgap35', 'Sertad1', 'Sertad2', 'Smyd1', 'Smyd3', 'Setdb1', 'Skil', 'Smad3', 'Smad5', 'Smad7', 'Smad9', 'Snw1', 'Spen', 'Sox1', 'Sox10', 'Sox11', 'Sox13', 'Sox14', 'Sox17', 'Sox18', 'Sox2', 'Sox21', 'Sox3', 'Sox4', 'Sox7', 'Sox8', 'Sox9', 'Sub1', 'Smarcal1', 'Smarca1', 'Smarca2', 'Smarca4', 'Smarca5', 'Smarcb1', 'Smarcc1', 'Smarcd1', 'Smarcd2', 'Smarcd3', 'Smarce1', 'Smarcad1', 'Sp7', 'Tal2', 'Tbx1', 'Tbx19', 'Tbx2', 'Tbx3', 'Tbx6', 'Tbpl1', 'Tmf1', 'Taf1', 'Taf10', 'Taf11', 'Taf12', 'Taf2', 'Taf3', 'Taf4b', 'Taf6l', 'Taf6', 'Taf7l', 'Taf7', 'Taf8', 'Taf9', 'Taf1a', 'Taf1c', 'Tead1', 'Tead2', 'Tead3', 'Tead4', 'Tgif1', 'Traf4', 'Tsc22d1', 'Txk', 'Snrpc', 'Snrpb2', 'Wdr5', 'Wwtr1', 'Wwox', 'Xbp1', 'Ybx1', 'Yy1', 'Ascl1', 'Ascc1', 'Atf2', 'Atf3', 'Atf4', 'Atf5', 'Atf6', 'Atf7ip', 'Atf7', 'Ar', 'Aatf', 'Apex1', 'Arx', 'Arnt2', 'Arnt', 'Arntl', 'Ahr', 'Atr', 'Atm', 'Atoh7', 'Atoh8', 'Relb', 'Bhlhe40', 'Bhlhe41', 'Blzf1', 'Bmp4', 'Bmp7', 'Bin1', 'Bptf', 'Brd4', 'Brd7', 'Abl1', 'Creb1', 'Creb3', 'Creb3l1', 'Creb3l4', 'Crem', 'Camta2', 'Cic', 'Ctnnb1', 'Cdc5l', 'Cdc6', 'Creg1', 'Clock', 'Ciita', 'Cbfa2t3', 'Cux1', 'Cux2', 'Dmtf1', 'Ccnt1', 'Ccnt2', 'Cdk7', 'Cdk8', 'Cdk9', 'Cdkn1a', 'Cdkn2a', 'Ddb1', 'Ddb2', 'Dcp1a', 'Dtx1', 'Dlx3', 'Dlx4', 'Dlx6', 'Ebf1', 'Egr1', 'Egr2', 'Egr3', 'Egr4', 'Eed', 'Emx2', 'Epas1', 'En1', 'En2', 'Ezh1', 'Ezh2', 'Esr1', 'Esrra', 'Esrrb', 'Esrrg', 'Ehf', 'Etv1', 'Etv3', 'Etv4', 'Etv5', 'Foxc1', 'Foxd1', 'Foxf2', 'Foxg1', 'Foxj2', 'Foxk1', 'Foxl2', 'Foxo1', 'Foxo3', 'Foxo4', 'Foxo6', 'Foxp1', 'Foxp2', 'Foxp4', 'Foxq1', 'Fosl1', 'Fosl2', 'Fhl2', 'Fus', 'Gbx2', 'Gtf2h1', 'Gtf2h2', 'Gtf2h4', 'Gtf2ird1', 'Gtf2i', 'Gtf2a1l', 'Gtf2b', 'Gtf3a', 'Gtf3c1', 'Gcm1', 'Gmeb1', 'Gmeb2', 'Grhl1', 'Grhl3', 'Gadd45a', 'Hes5', 'Hes6', 'Hes7', 'Hey1', 'Hsf1', 'Hsbp1', 'Hsf4', 'Hltf', 'Hlf', 'Hnrnpab', 'Hnrnpc', 'Hnrnpdl', 'Hnrnph2', 'Hnrnpk', 'Hnrnpl', 'Hnrnpll', 'Hnrnpu', 'Hmga1', 'Hmgb1', 'Hmgb2', 'Hmgn1', 'Hmgn3', 'Hdac1', 'Hdac10', 'Hdac2', 'Hdac3', 'Hdac4', 'Hdac5', 'Hdac6', 'Hdac7', 'Hdac9', 'Hoxa1', 'Hcfc1', 'Hivep1', 'Hivep2', 'Hic1', 'Hif1a', 'Id1', 'Id3', 'Id4', 'Itgb3bp', 'Ifi204', 'Irf1', 'Irf2bp1', 'Irf2', 'Irf3', 'Irf5', 'Irf7', 'Irf9', 'Jade1', 'Junb', 'Jund', 'Jun', 'Lztfl1', 'Lcor', 'Lef1', 'Kdm5a', 'Kdm5b', 'Kdm5c', 'Kmt2a', 'Maml1', 'Maml3', 'Mnt', 'Med1', 'Med12', 'Med14', 'Med15', 'Med17', 'Med21', 'Med23', 'Med24', 'Med6', 'Mnat1', 'Mn1', 'Mtf1', 'Mtf2', 'Mta1', 'Mta2', 'Mecp2', 'Mbd1', 'Mbd2', 'Mitf', 'Mapk14', 'Mapk3', 'Mapk7', 'Msx1', 'Msx2', 'Msc', 'Myef2', 'Myt1', 'Myt1l', 'Myb', 'Mybl1', 'Mybl2', 'Myc', 'Mzf1', 'Mllt1', 'Mllt11', 'Mllt3', 'Mllt6', 'Mef2a', 'Mef2b', 'Mef2d', 'Myog', 'Nhlh2', 'Nrg1', 'Neurod1', 'Neurod2', 'Neurod6', 'Npas1', 'Npas2', 'Npas4', 'Notch1', 'Notch2', 'Notch3', 'Sp100', 'Nfic', 'Nfix', 'Nfat5', 'Nfatc1', 'Nfatc2', 'Nfatc3', 'Nfatc4', 'Nfkb1', 'Nfkb2', 'Nfkbia', 'Nfkbie', 'Nfkbiz', 'Nfe2l3', 'Nfe2l1', 'Nfil3', 'Nufip1', 'Ncor1', 'Ncor2', 'Ncoa1', 'Ncoa2', 'Ncoa3', 'Ncoa4', 'Ncoa5', 'Ncoa6', 'Ncoa7', 'Nrip1', 'Nrip3', 'Nr0b1', 'Nr1d1', 'Nr1d2', 'Nr1h2', 'Nr1h3', 'Nr1i2', 'Nr2c1', 'Nr2c2', 'Nr2e1', 'Nr2f1', 'Nr2f2', 'Nr2f6', 'Nr3c1', 'Nr3c2', 'Nr4a1', 'Nr4a2', 'Nr4a3', 'Nr5a1', 'Nr5a2', 'Nr6a1', 'Nsd1', 'Nrf1', 'Nfx1', 'Nfya', 'Nfyb', 'Npm1', 'Olig1', 'Olig2', 'Onecut1', 'Onecut2', 'Onecut3', 'Otx2', 'Ovol2', 'Pax1', 'Pax3', 'Pax5', 'Pax6', 'Pax8', 'Pax9', 'Prrx1', 'Prrx2', 'Prrxl1', 'Pitx2', 'Per1', 'Per2', 'Per3', 'Ppargc1b', 'Ppara', 'Pparg', 'Ppard', 'Pgs1', 'Plag1', 'Plagl1', 'Plagl2', 'Parp1', 'Parp12', 'Pabpn1', 'Pcbp1', 'Pcbp2', 'Pcbp4', 'Pbrm1', 'Pcgf2', 'Pcgf6', 'Pqbp1', 'Polr2a', 'Polr2b', 'Polr2k', 'Pbx3', 'Pbx4', 'Pgr', 'Pdcd7', 'Phb2', 'Preb', 'Pml', 'Psmc5', 'Pin1', 'Pias1', 'Pias2', 'Pias3', 'Pias4', 'Pcbd1', 'Pura', 'Purb', 'Phtf1', 'Rreb1', 'Rbpj', 'Rsl1', 'Rfx1', 'Rfx2', 'Rfx4', 'Rfx5', 'Rfxank', 'Rfxap', 'Rel', 'Rax', 'Rb1', 'Rbbp4', 'Rbbp7', 'Rbl1', 'Rara', 'Rarb', 'Rarg', 'Rxra', 'Rxrb', 'Rpl7', 'Rpl7a', 'Ring1', 'Rnf14', 'Rnf2', 'Rnf25', 'Rnf4', 'Runx1', 'Runx2', 'Runx1t1', 'Sall1', 'Sall2', 'Sall3', 'Sall4', 'Safb', 'Safb2', 'Scx', 'Scrt1', 'Srf', 'Scmh1', 'Scml2', 'Stat1', 'Stat3', 'Stat4', 'Stat5a', 'Stat5b', 'Stat6', 'Six3', 'Sim2', 'Sirt3', 'Ski', 'Snapc4', 'Snai1', 'Snai2', 'Satb1', 'Satb2', 'Spi1', 'Sra1', 'Srebf2', 'Srebf1', 'Strn', 'Ssrp1', 'Supt16', 'Supt4a', 'Supt5', 'Suv39h1', 'Suv39h2', 'Suz12', 'Tdg', 'Tox', 'Thra', 'Thrap3', 'Thrb', 'Trip11', 'Tef', 'Topors', 'Sp1', 'Sp3', 'Sp4', 'Tcea2', 'Tcf12', 'Tcf20', 'Tcf3', 'Tcf7l1', 'Tcf7l2', 'Tcf7', 'Tfam', 'Tfap4', 'Tfcp2', 'Tfdp1', 'Tfdp2', 'Tfe3', 'Tfec', 'Ttf1', 'Tada3', 'Sin3a', 'Sin3b', 'Tbl1x', 'Tle1', 'Tle2', 'Tle3', 'Tle4', 'Tle6', 'Trp53', 'Trp63', 'Trrap', 'Mdm2', 'Mdm4', 'Tgfb1i1', 'Tsn', 'Tgs1', 'Trim24', 'Trim25', 'Trim28', 'Trim30a', 'Trim33', 'Tsc2', 'Ubp1', 'Ubtf', 'Usf1', 'Usf2', 'Uaca', 'Mafa', 'Mafb', 'Maff', 'Mafg', 'Mafk', 'Rela', 'Vps72', 'Vezf1', 'Vax1', 'Vgll2', 'Vsx1', 'Vdr', 'Vhl', 'Zrsr1', 'Zc3h8', 'Zeb1', 'Zeb2', 'Zbtb14', 'Zbtb16', 'Zbtb17', 'Zbtb33', 'Zbtb7b', 'Zscan21', 'Zfp106', 'Zfp110', 'Zfp128', 'Zfp148', 'Zfp239', 'Zfp263', 'Zfp326', 'Zfp354a', 'Zfp422', 'Zfp423', 'Zfp467', 'Zfp472', 'Zfp503', 'Zfp60', 'Zfp64', 'Zfp668', 'Zfp68', 'Zfp90', 'Zik1', 'Zic1', 'Zic2', 'Zic3', 'Zic4', 'Zic5', 'Zfpm1', 'Zfpm2', 'Zkscan17', 'Zmym2', 'Zmynd10', 'Zhx1', 'Zhx2', 'Adnp', 'Aebp1', 'Aebp2', 'Ahrr', 'Aip', 'Alx3', 'Alx4', 'Ank1', 'Ankfy1', 'Atf1', 'Bard1', 'Bnc1', 'Brca1', 'Zfp36l1', 'Camk4', 'Cbfb', 'Cbx3', 'Cbx4', 'Ccnk', 'Cdk2', 'Cdk4', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d', 'Chgb', 'Dach1', 'Dlx1', 'Dnmt1', 'Dnmt3a', 'Dr1', 'Drg1', 'Ebf2', 'Ebf3', 'Ell', 'Ercc2', 'Ercc3', 'Erg', 'Etv6', 'Bcl11a', 'Fah', 'Fem1a', 'Fem1b', 'Foxc2', 'Foxn2', 'Gabpb1', 'Gas7', 'Mtpn', 'Baz1a', 'Hes1', 'Hey2', 'Foxj1', 'Hipk2', 'Hira', 'Hmgn2', 'Hmg20b', 'Hmgb3', 'Hmga2', 'Hmx1', 'Hsf2', 'Htr5a', 'Elavl2', 'Ifnar2', 'Ikbkb', 'Ikbkg', 'Ilf3', 'Invs', 'Irx1', 'Irx2', 'Klf2', 'Klf3', 'Hivep3', 'Ldb1', 'Ldb2', 'Lhx5', 'Lhx8', 'Lmo2', 'Lmo4', 'Lyl1', 'Maf', 'Mbd3', 'Mcm3', 'Mcm2', 'Mcm4', 'Mcm5', 'Mcm6', 'Mcm7', 'Mef2c', 'Rab8a', 'Men1', 'Mid1', 'Mllt10', 'Mpl', 'Nab1', 'Naca', 'Ndn', 'Nedd8', 'Nfatc2ip', 'Nfe2l2', 'Nfkbib', 'Nfkbil1', 'Nfyc', 'Nkx2-2', 'Notch4', 'Uhrf1', 'Musk', 'Sqstm1', 'Otp', 'Otx1', 'Pbx2', 'Pdcd11', 'Enpp2', 'Peg3', 'Phf2', 'Papola', 'Cnot7', 'Pou3f3', 'Ppargc1a', 'Scand1', 'Ppp1r1b', 'Ppp3cb', 'Ppp5c', 'Prkar1a', 'Prox1', 'Psmc3', 'Ptma', 'Rab11b', 'Rab18', 'Rai1', 'Rbl2', 'Rfx3', 'Rnps1', 'Rorc', 'Polr2c', 'Polr2j', 'Ruvbl2', 'Rxrg', 'Khdrbs1', 'Sap18', 'Ostf1', 'Shox2', 'Siah2', 'Sim1', 'Six5', 'Six6', 'Ighmbp2', 'Snrpb', 'Sox12', 'Zfp143', 'Stag1', 'Stat2', 'Strap', 'Taf1b', 'Tbp', 'Tbx15', 'Tbx4', 'Tcea1', 'Tcea3', 'Phf1', 'Thrsp', 'Tial1', 'Tmpo', 'Tnfaip3', 'Dedd', 'Ppp1r13b', 'Trip6', 'Tob1', 'Trp73', 'Tsg101', 'Baz1b', 'Zfp1', 'Zfp105', 'Zfp13', 'Zfp2', 'Zfp207', 'Zfp27', 'Zfp35', 'Zfp36', 'Zfp37', 'Zfp39', 'Zfp40', 'Zfp41', 'Zfp46', 'Zfp51', 'Zfp52', 'Zfp54', 'Zfp57', 'Zfp59', 'Zfp61', 'Zfp9', 'Zfp92', 'Zfp93', 'Zfp94', 'Zfp97', 'Zfx', 'Zim1', 'Ash2l', 'Bpnt1', 'Dido1', 'Dazap2', 'Ing1', 'Map3k12', 'Mapk1', 'Mapk8', 'Zfp146', 'Cops5', 'Polr3e', 'Sh3d19', 'Zfp275', 'Trp53bp1', 'Zfp354b', 'Ing4', 'Limd1', 'Mga', 'Zfp292', 'Pdlim4', 'Hnf4g', 'Zfp354c', 'Abt1', 'Cbx8', 'Sirt6', 'Fbxw7', 'Nrbf2', 'Lsm4', 'Dmrt1', 'Keap1', 'Ppp2r1a', 'Ccnc', 'Ppp1r10', 'Carhsp1', 'Plrg1', 'Pla2g6', 'Psmd10', 'Htatip2', 'Hif3a', 'Rab25', 'Rab11a', 'Ddx20', 'Deaf1', 'Pdlim1', 'Irx5', 'Lrrc6', 'Zfp108', 'Tcerg1', 'Heyl', 'Zfp386', 'Rbm14', 'Nupr1', 'Zfp113', 'Trip4', 'Ruvbl1', 'Papolb', 'Pmfbp1', 'Zfp235', 'Rps6ka4', 'Zfp111', 'Usp49', 'Zfp109', 'Zfp112', 'Jmy', 'Fhl5', 'Hic2', 'Shank3', 'Carm1', 'Nrip2', 'Sap30', 'Foxb1', 'Sirt2', 'Nmi', 'Asb1', 'Asb4', 'Asb3', 'Znrd1', 'Dmap1', 'Polr2e', 'Polr2l', 'Zmynd11', 'Mcm8', 'Ctnnbl1', 'Ccnh', 'Hmg20a', 'Fank1', 'Ssbp2', 'Zdhhc6', 'Yaf2', 'Polr3d', 'Nrarp', 'Rnf141', 'Psmd9', 'Pfdn1', 'Zfp606', 'Dedd2', 'Xab2', '4930430A15Rik', 'Rabl3', 'Polr2g', 'Sec14l2', 'Sncaip', 'Nudt12', 'Gtf2e2', 'Zdhhc21', 'Sirt5', 'Phf5a', 'Ankra2', 'Gtf2f2', 'Tulp4', 'Zfp580', 'Cnot8', 'Ppp1r12b', 'Trip13', 'Zfp219', 'Polr2i', '2810021J22Rik', 'Dpf3', 'Basp1', 'Asb12', 'Polr3b', 'Arhgap17', 'Bbx', 'Zcchc8', 'Prdm16', 'Prdm5', 'Zdhhc1', '4931423N10Rik', 'Zfp597', 'Fbxo24', '4933429H19Rik', 'Fmnl2', 'Pogk', 'Ing3', 'Phf7', 'Ddx54', 'Phf10', 'Lsm11', 'Nkd2', 'Asb6', 'Zfp131', 'Ssbp3', 'Bclaf1', 'Zfp248', 'Wasl', 'Nptxr', 'Hbp1', 'Rnf38', 'Btbd11', 'Zdhhc16', 'Gtf2e1', 'Cxxc1', 'Xrcc3', 'Zfp84', 'Hsf2bp', 'Prr3', 'Zbtb3', 'Sirt4', '1700003F12Rik', 'Acyp2', 'Rai14', 'Asb5', 'Tbx18', 'Snip1', 'Ssbp4', 'Zfp142', 'Lbh', '4930522L14Rik', 'Helz', 'Asb8', 'Brd8', 'Brpf1', 'Asb15', 'Sp2', '9130019O22Rik', 'Polr3h', 'Zfp319', 'Abtb1', 'Mynn', 'Zfp202', 'Sp6', 'Gtf2a1', 'Sirt1', 'Dach2', 'Maged1', 'Ptges2', 'Zfp451', 'Taf13', 'Zfp282', 'Taf5l', 'Snapc2', 'Ankrd10', 'Rcor1', 'Rab15', 'Tcf19', 'Bmyc', 'Chd4', 'Zdhhc15', 'Ankhd1', 'Uhrf2', 'Lmo1', 'Zfp91', 'Dek', 'Dbx2', 'Lmx1a', 'Brdt', 'Pawr', 'Baz2a', 'Mta3', 'Ivns1abp', 'Asb10', 'Ankrd6', 'Rem2', 'Ubn1', 'Zfp287', 'Zfp617', 'Ash1l', 'Clp1', 'Ell2', 'Dot1l', 'Hmgcs1', 'Sirt7', 'Zfp180', '6720489N17Rik', 'Zswim4', 'Myocd', 'L3mbtl2', 'Polrmt', 'Gls2', 'Ugp2', 'Btf3', 'Zfp523', 'Trerf1', 'Taf5', 'Zfp281', 'Pms1', 'Zdhhc5', 'Ebf4', 'Nkx2-4', 'Asxl1', 'Tgif2', 'Ppp1r16b', 'Zfp334', 'Pbxip1', 'Ddx58', 'Zfp189', 'Glis1', 'Tardbp', 'Phf13', 'Creb5', 'Hdac11', 'Ppp1r12c', 'Zfp592', 'Zfp553', 'Zfp612', 'Zfp426', 'Rab8b', 'Scap', 'Tceal1', 'BC024063', 'Zfp454', 'Homez', 'Fem1c', 'Setbp1', 'St18', 'Carf', 'Dmrta2', 'Bsx', 'Polr2h', 'Zfp277', 'Obox3', 'Shprh', 'Phf12', 'Zbtb1', 'Caskin1', 'Gtf3c4', 'Gcdh', 'BC024139', 'Zfp398', 'A630089N07Rik', 'Phf8', 'Zhx3', 'Zfp82']
['Alx1', 'Barhl1', 'Elf3', 'Fev', 'Gata1', 'Gata3', 'Gata5', 'Gata6', 'Hnf1a', 'Irx6', 'Mixl1', 'Nkx2-3', 'Nkx2-9', 'Nobox', 'Spdef', 'Sox15', 'Spib', 'Tal1', 'Tbx20', 'Tbx21', 'Tbr1', 'Wt1', 'Ascl3', 'Aire', 'Batf', 'T', 'Creb3l3', 'Cdx1', 'Crxos', 'Dmbx1', 'Dlx2', 'Dlx5', 'Emx1', 'Esr2', 'Esx1', 'Figla', 'Foxa1', 'Foxd2', 'Foxf1', 'Foxm1', 'Foxp3', 'Gcm2', 'Gsc', 'Gfi1', 'Hes3', 'Hhex', 'Hnf4a', 'Hoxa2', 'Hoxa5', 'Hoxa7', 'Hoxb4', 'Hoxb5', 'Hoxc6', 'Hesx1', 'Irf8', 'Meox1', 'Meox2', 'Mesp2', 'Myod1', 'Nrl', 'Neurod4', 'Noto', 'Nfe2', 'Nr1h4', 'Nr1h5', 'Nr1i3', 'Ovol1', 'Pax2', 'Pax7', 'Phox2a', 'Pitx1', 'Pitx3', 'Ptf1a', 'Pdx1', 'Rbpjl', 'Rhox5', 'Rhox8', 'Runx3', 'Six2', 'Six4', 'Snai3', 'Tcf15', 'Tfap2b', 'Tfap2a', 'Tfap2e', 'Trib3', 'Twist1', 'Twist2', 'Mycn', 'Vav1', 'Vsx2', 'Zfy1', 'Neurog2', 'Bcl6b', 'Cbx2', 'Foxb2', 'Glrp1', 'Hcls1', 'Foxn1', 'Hlx', 'Hmx3', 'Foxa3', 'Irf4', 'Lhx6', 'Lhx9', 'Ascl2', 'Msx3', 'Pou2f3', 'Zfy2', 'Nr0b2', 'Vax2', 'Insm1', 'Irf6', 'Hils1', 'Ankrd2', 'Insm2', 'Sp5', 'Asb2', '1700020N01Rik', 'Asb11', 'Dmrtc2', '1700086D15Rik', 'Hspb9', '2410141K09Rik', 'Ankrd1', 'Foxn4', 'Sox30', 'Gbx1', 'Dmrta1', 'Tcfl5']

for gene in tfcols:
    sc.pl.umap(
        adata_eval,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        vmax="p90.0",
        na_color="#9A9FB080",
        save="-tf1-" + gene + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf1-Ddx5-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

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WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf1-Sox5-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

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WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf1-Son-replic2-best-subregional_supervised-xgboost-evaluation-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

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for gene in tfcols2:
    sc.pl.umap(
        adata_eval,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        na_color="#9A9FB080",
        save="-tf2" + gene + save_PRJNA779749,
)

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for gene in tfcols3:
    sc.pl.umap(
        adata_eval,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
        vmin=0.0,
        na_color="#9A9FB080",
        save="-tf3" + gene + save_PRJNA779749,
)

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Additional validation of whole hypothalamus predictions

PRJNA847050_etr = PRJNA847050_counts[PRJNA847050_counts.obs["train"]]
PRJNA815819_etr = PRJNA815819_counts[PRJNA815819_counts.obs["train"]]
PRJNA798401_etr = PRJNA798401_counts[PRJNA798401_counts.obs["train"]]
PRJNA723345_etr = PRJNA723345_counts[PRJNA723345_counts.obs["train"]]
PRJNA722418_etr = PRJNA722418_counts[PRJNA722418_counts.obs["train"]]
PRJNA705596_etr = PRJNA705596_counts[PRJNA705596_counts.obs["train"]]
PRJNA679294_etr = PRJNA679294_counts[PRJNA679294_counts.obs["train"]]
PRJNA611624_etr = PRJNA611624_counts[PRJNA611624_counts.obs["train"]]
PRJNA604055_etr = PRJNA604055_counts[PRJNA604055_counts.obs["train"]]
PRJNA548532_etr = PRJNA548532_counts[PRJNA548532_counts.obs["train"]]
PRJNA515063_etr = PRJNA515063_counts[PRJNA515063_counts.obs["train"]]
PRJNA453138_etr = PRJNA453138_counts[PRJNA453138_counts.obs["train"]]

traindata = concat(
    [
        PRJNA847050_etr,
        PRJNA815819_etr,
        PRJNA798401_etr,
        PRJNA722418_etr,
        PRJNA705596_etr,
        PRJNA679294_etr,
        PRJNA611624_etr,
        PRJNA604055_etr,
        PRJNA548532_etr,
        PRJNA515063_etr,
        PRJNA453138_etr,
    ],
    join="outer",
)

traindata = traindata[
    :,
    ~traindata.var_names.str.match(r"(^Hla-|^Ig[hjkl]|^Rna|^mt-|^Rp[sl]|^Hb[^(p)]|^Gm)"),
]
traindata = traindata[:, ~traindata.var_names.isin(hk_genes1)]

sc.pp.filter_genes(traindata, min_cells=3)
sc.pp.normalize_total(traindata, target_sum=1e4)
sc.pp.log1p(traindata)
sc.pp.regress_out(traindata, ["nCount_RNA", "percent_mito_ribo"])


X = pd.DataFrame(
    traindata.X, columns=traindata.var_names, index=traindata.obs_names
)

X_uni = X.loc[:, uni_fs]
X_uni

filtered out 3103 genes that are detected in less than 3 cells

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/preprocessing/_simple.py:251: ImplicitModificationWarning:

Trying to modify attribute `.var` of view, initializing view as actual.

normalizing counts per cell

    finished (0:00:00)

regressing out ['nCount_RNA', 'percent_mito_ribo']

    sparse input is densified and may lead to high memory use

    finished (0:01:31)

	0610005C13Rik	Lpl	Lpp	Lrat	Lrba	Lrch3	0610043K17Rik	Lrguk	Lrig1	1110008P14Rik	...	Lncpint	Lnpep	Lonrf2	Lor	Loxhd1	Loxl2	Lpar1	Lpar3	Lpgat1	Lpin2
SRR19578591_AAAGTCCAGCGCTGAA-1	-0.003658	-0.097231	0.397235	1.724018	0.775146	-0.417920	-0.045263	-0.009627	-1.128234	0.720752	...	1.361610	0.729863	-0.204225	-0.024330	0.000011	-0.020054	1.024486	-0.001250	-0.066735	-0.275885
SRR19578591_AACAGGGGTCGAACAG-1	-0.009527	-0.080301	1.452114	2.076940	-0.508951	1.590362	1.661251	-0.017726	-1.239897	-0.382163	...	2.613074	1.245023	-0.196646	-0.017837	-0.000207	-0.019083	-0.150634	-0.002218	-0.072420	-0.306376
SRR19578591_AACAGGGTCCTCTTTC-1	-0.020677	-0.054399	0.102160	-0.007604	0.475244	0.526976	-0.128827	-0.033101	1.179204	-0.233782	...	0.973193	-0.548536	-0.187190	-0.005535	-0.000619	-0.017642	-0.119571	-0.004042	-0.086366	-0.370693
SRR19578591_AACCTTTAGGGAGAAT-1	-0.011262	-0.034327	0.802863	-0.003413	1.142423	-0.449374	-0.070527	-0.020180	1.438533	-0.181004	...	2.157700	-0.417324	-0.162073	-0.015706	-0.000287	-0.016150	-0.100664	-0.002589	-0.053511	1.354772
SRR19578591_AACGTCAAGGACCCAA-1	-0.017768	-0.049652	0.283848	-0.006310	-0.625722	-0.579115	-0.111219	-0.029108	0.789893	-0.223649	...	-0.626552	-0.510829	1.092922	-0.008685	-0.000516	-0.017275	-0.115294	-0.003590	-0.076947	1.823469
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
SRR7059023_TTCCCAGAGAGTGAGA-1	-0.021037	-0.013669	-0.983337	-0.007759	-0.643287	-0.579337	-0.119612	-0.033657	1.176740	-0.059619	...	-0.636934	1.109590	-0.155401	-0.004931	-0.000647	-0.015020	-0.075637	-0.004184	-0.066768	-0.331977
SRR7059023_TTGCGTCTCTTACCTA-1	-0.025537	0.004938	-1.037531	-0.009758	-0.704781	-0.625629	-0.139696	-0.039874	0.276772	0.034880	...	-0.714158	-0.480768	-0.145150	0.000075	-0.000817	-0.013912	-0.054327	-0.004938	-0.068298	1.352663
SRR7059023_TTGGCAACATAAAGGT-1	-0.024316	0.029538	-0.962534	-0.009212	-0.652321	-0.568982	-0.126069	-0.038231	-1.316606	0.135112	...	-0.632870	-0.420991	-0.124533	-0.001130	-0.000782	-0.012298	-0.028204	-0.004795	-0.052983	-0.314495
SRR7059023_TTTATGCCACGGTTTA-1	-0.020189	0.021684	2.409572	-0.007377	2.061803	-0.512830	-0.105109	-0.032542	-1.225768	0.087551	...	-0.543301	-0.395851	-0.126665	-0.005674	-0.000630	-0.012720	-0.037835	-0.004124	-0.046951	-0.288918
SRR7059023_TTTGCGCAGAGTACCG-1	-0.020370	0.019993	-0.901240	-0.007458	-0.590227	-0.518320	-0.106590	-0.032789	-1.235954	0.080994	...	-0.551373	-0.400912	-0.128178	-0.005485	-0.000636	-0.012833	-0.039604	-0.004149	-0.048239	-0.292122

8679 rows × 4217 columns

y = traindata.obs["region"]

# Compute the proportion of each category
prop = y.value_counts(normalize=True)

# Sort the categories by their proportion
cat = pd.Categorical(y, categories=prop.index.sort_values(), ordered=True)

# Get the integer codes of the categories
codes = cat.codes

Hajdarovic et al. 2021

PRJNA779749_counts = sc.read_h5ad(
    f"data/PRJNA779749/data/PRJNA779749-astrocytes_dataset-{cb_fpr}-scenic_plus.h5ad"
)

PRJNA779749_regulons = sc.read_h5ad(
    f"data/PRJNA779749/data/PRJNA779749-astrocytes_dataset-{cb_fpr}-regulons.h5ad"
)

adata_eval2 = PRJNA779749_counts.copy()
# sc.pp.filter_genes(adata_eval2, min_cells=5) # filter genes should be matched with training set
sc.pp.normalize_total(adata_eval2, target_sum=1e4)
sc.pp.log1p(adata_eval2)
sc.pp.regress_out(adata_eval2, ["nCount_RNA", "percent_mito_ribo"])

normalizing counts per cell

    finished (0:00:00)

regressing out ['nCount_RNA', 'percent_mito_ribo']

    sparse input is densified and may lead to high memory use

    finished (0:01:20)

df_eval2 = pd.DataFrame(
    adata_eval2.X, columns=adata_eval2.var_names, index=adata_eval2.obs_names
)

df_eval2 = df_eval2.loc[
    :, [g for g in uni_fs if g in adata_eval2.var_names]
]
df_eval2.head()

	0610005C13Rik	Lpl	Lpp	Lrat	Lrba	Lrch3	0610043K17Rik	Lrguk	Lrig1	1110008P14Rik	...	Lncpint	Lnpep	Lonrf2	Lor	Loxhd1	Loxl2	Lpar1	Lpar3	Lpgat1	Lpin2
SRR16922352_AAACGGGTCGAATGCT-1	-0.018331	-0.005336	2.500953	-0.009255	-0.609590	-0.508090	-0.226868	-0.022867	-0.954689	-0.001990	...	-0.780364	1.465346	-0.060129	-0.000441	-0.000287	-0.067778	-0.062805	-0.001580	-0.060612	-0.231321
SRR16922352_AAAGCAAAGGACCACA-1	-0.016664	-0.005518	-0.802946	-0.006173	-0.554583	-0.457097	2.066583	-0.016105	-0.909527	-0.000572	...	-0.683872	-0.355049	-0.043341	-0.000301	-0.000432	-0.055866	-0.050833	-0.001095	-0.033836	-0.198344
SRR16922352_AAAGCAATCAGCTTAG-1	-0.022545	-0.004682	-0.864842	-0.004707	1.418340	-0.511443	-0.242264	-0.017473	0.988217	0.000125	...	-0.768795	-0.378361	-0.044779	-0.000077	-0.000544	-0.079565	-0.044637	-0.000721	-0.033543	-0.229535
SRR16922352_AACACGTTCCTTTACA-1	-0.028544	-0.003885	0.666457	-0.006691	0.909304	-0.602281	-0.273678	-0.025209	0.433491	-0.000771	...	0.693270	-0.444942	-0.062540	-0.000050	-0.000483	-0.108906	-0.051975	-0.000926	-0.059966	-0.285362
SRR16922352_AACCGCGTCCACGCAG-1	-0.023295	-0.004576	1.128169	-0.004560	-0.621269	-0.518777	-0.245747	-0.017719	-1.060626	0.000196	...	-0.780445	-0.381821	-0.045148	-0.000050	-0.000556	-0.082646	-0.044002	-0.000680	-0.033810	-0.233786

5 rows × 4200 columns

X_uni_to_PRJNA779749 = X_uni.loc[:, [g for g in uni_fs if g in adata_eval2.var_names]]
subregional_model.fit(X_uni_to_PRJNA779749, codes)

joblib.dump(
    subregional_model,
    f"models/svm/best-logit-subregional_PRJNA779749_astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle",
)

print(X_uni_to_PRJNA779749.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist())

['0610005C13Rik', '0610043K17Rik', '1190005I06Rik', 'Lrp8os2', '1500004A13Rik', '1500015L24Rik', '1600010M07Rik', '1600012H06Rik', 'Lrrc45', 'Lrrc57', '1700003D09Rik', '1700003G18Rik', '1700003M07Rik', '1700008C04Rik', 'Lrrd1', 'Lrrk1', 'Lsm8', '1700022H01Rik', '1700024B05Rik', 'Ltbp1', '1700025F22Rik', '1700027I24Rik', '1700028E10Rik', '1700029I15Rik', '1700030F04Rik', 'Ly86', '1700030L22Rik', '1700030N03Rik', '1700034I23Rik', 'Lysmd4', 'M1ap', '1700054A03Rik', '1700060O08Rik', '1700061G19Rik', '1700064H15Rik', 'Mageb18', '1700081N11Rik', '1700084C06Rik', '1700095A13Rik', '1700095J03Rik', 'Mamdc2', 'Mamdc4', 'Maml3', '1700102P08Rik', '1700108F19Rik', 'Mannr', 'Maob', 'Map1b', '1700125G22Rik', 'Map2k4', 'Map3k11', '1810020O05Rik', '1810030O07Rik', 'Map4k3', 'Map7', '2010007H06Rik', '2210016F16Rik', '2310030A07Rik', '2310030G06Rik', '2310039L15Rik', 'Masp1', '2310068J16Rik', '2510009E07Rik', '2610001J05Rik', '2600006K01Rik', 'Mboat4', '2610307P16Rik', 'Mccc1os', '2610318N02Rik', '2700033N17Rik', 'Mcm6', '2810030D12Rik', '2810410L24Rik', 'Mcur1', 'Mdfi', '2810459M11Rik', '2900052N01Rik', 'Mdp1', '3110021N24Rik', '3110039M20Rik', '3110056K07Rik', '3200001D21Rik', '3222401L13Rik', 'Mef2c', 'Meg3', '4731419I09Rik', 'Meikin', 'Meis2', '4732496C06Rik', '4833422C13Rik', 'Metap1d', '4833423E24Rik', 'Mettl11b', 'Mettl17', '4921507P07Rik', '4921511C10Rik', 'Mettl24', '4921539H07Rik', '4930404I05Rik', '4930404N11Rik', 'Mfng', 'Mfsd14b', 'Mfsd2a', 'Mfsd3', '4930415C11Rik', '4930415O20Rik', 'Mfsd7a', 'Mgam', 'Mgam2-ps', '4930432B10Rik', '4930439D14Rik', '4930441H08Rik', '4930445B16Rik', '4930447C04Rik', 'Mid1', '4930451E10Rik', 'Miip', 'Mill2', '4930455B14Rik', '4930467E23Rik', '4930470G03Rik', 'Mir124-2hg', '4930473A02Rik', 'Mlh3', '4930512B01Rik', 'Mmp11', 'Mmrn2', '4930529K09Rik', 'Mogat1', '4930545L23Rik', 'Mok', '4930549C15Rik', 'Morn2', '4930554G24Rik', '4930554H23Rik', '4930555B11Rik', 'Morn5', '4930556G01Rik', '4930556J24Rik', 'Mpg', 'Mphosph9', 'Mpped1', 'Mpped2', '4930589L23Rik', 'Mrgpra6', '4930594M22Rik.1', '4931403E22Rik', 'Mroh7', '4931413K12Rik', '4932411K12Rik', '4932416K20Rik', '4932435O22Rik', '4932443L11Rik', 'Mrpl42', '4933403O08Rik', '4933406F09Rik', 'Mrps18b', '4933408J17Rik', 'Msantd2', 'Msh6', '4933431E20Rik', 'Msl3', '4933433H22Rik', 'Mta1', 'Mta3', '5031434O11Rik', 'Mtcp1', '5033426O07Rik', 'Mtfp1', 'Mtg1', '5430416N02Rik', 'Mthfs', '5430431A17Rik', 'Mtmr4', '5730522E02Rik', '5830444B04Rik', 'Mttp', 'Mtus1', '5930435M05Rik', '6030498E09Rik', '6330403K07Rik', 'Mus81', 'Mvd', '6430503K07Rik', '6330576A10Rik', '6430553K19Rik', '6430628N08Rik', '6530402F18Rik', '6530409C15Rik', '6530411M01Rik', '6720427I07Rik', 'Mycbp', 'Mycl', '8030453O22Rik', 'Myh6', 'Mylip', '9130008F23Rik', '9130017K11Rik', '9130221H12Rik', 'Myo1f', 'Myoc', 'Myorg', '9330158H04Rik', 'Myrfl', 'Myrip', '9330199G10Rik', 'Naa10', '9630001P10Rik', '9630014M24Rik', 'Naip1', '9630028H03Rik', 'Nalcn', 'Napb', 'A1bg', 'A230020J21Rik', 'A230051N06Rik', 'Nat8f7', 'Naxe', 'A230108P19Rik', 'A330076C08Rik', 'A330087D11Rik', 'A330094K24Rik', 'A4galt', 'Ncoa6', 'Ncor2', 'A530053G22Rik', 'Ndnf', 'A630001O12Rik', 'A630012P03Rik', 'A630023A22Rik', 'A730020M07Rik', 'A730049H05Rik', 'Ndufaf4', 'A730063M14Rik', 'A830035A12Rik', 'Necab3', 'Necap1', 'A930002C04Rik', 'Nectin1', 'A930006K02Rik', 'Nefh', 'Nek11', 'Nek8', 'AA465934', 'AB124611', 'Nell2', 'AC113595.1', 'AC163638.2', 'AI429214', 'AI463229', 'Nfic', 'AI849053', 'Nfkbia', 'Nfkbib', 'Nfkbil1', 'AU023762', 'AW146154', 'Nhs', 'AY036118', 'Nifk', 'Aarsd1', 'Aasdhppt', 'Aass', 'Aatk', 'Nkain2', 'Nkd1', 'Nkx2-1', 'Nkx2-2', 'Nkx2-2os', 'Nkx2-4', 'Abcb1a', 'Abcb9', 'Nlgn2', 'Abcc10', 'Abcc2', 'Abcf2', 'Nme3', 'Abhd15', 'Nmnat2', 'Nmur1', 'Abl1', 'Ablim2', 'Acap3', 'Notum', 'Acat2', 'Acat3', 'Ackr3', 'Ackr4', 'Acp1', 'Acsbg2', 'Nr1d1', 'Nr1d2', 'Nr2f2', 'Actr3b', 'Acvr1c', 'Acy3', 'Adam11', 'Nrg2', 'Adam5', 'Adamts12', 'Nsd2', 'Nsdhl', 'Nsf', 'Adamts2', 'Nsmce1', 'Nsmce3', 'Adat2', 'Ntn1', 'Ntn3', 'Ntng1', 'Nuak2', 'Adgrb2', 'Adgrf4', 'Adgrg2', 'Adgrl2', 'Nudt16l1', 'Adra1b', 'Adrb1', 'Nupr1l', 'Aff3', 'Afm', 'Agap1', 'Agap2', 'Agbl1', 'Nxpe5', 'Oacyl', 'Ahctf1', 'Ahdc1', 'Ahsa1', 'Ogdhl', 'Ogfod2', 'Ogfod3', 'Ajm1', 'Olfml2b', 'Olfr1511', 'Akr1b3', 'Olfr455', 'Akt2', 'Olfr653', 'Olfr655', 'Olfr656', 'Olfr66', 'Olig2', 'Omg', 'Onecut2', 'Opa1', 'Alkal2', 'Alkbh7', 'Alpl', 'Alyref2', 'Amdhd2', 'Amer2', 'Ammecr1', 'Amot', 'Otoa', 'Amy1', 'Angel1', 'Otx1', 'Otx2', 'Otx2os1', 'Ovca2', 'Angptl4', 'Ankfn1', 'P2rx3', 'P2ry12', 'P2ry13', 'P2ry6', 'Ankrd22', 'P3h4', 'Pacsin1', 'Pacsin2', 'Padi2', 'Paf1', 'Anks1b', 'Ankzf1', 'Anln', 'Pank1', 'Paox', 'Anxa9', 'Aox1', 'Pard3bos1', 'Ap5s1', 'Apc2', 'Pawr', 'Pax5', 'Apln', 'Apmap', 'Apobec4', 'Aqp5', 'Pcdhb2', 'Aqp9', 'Ar', 'Pcdhb22', 'Pcgf1', 'Pck2', 'Arg2', 'Arhgap23', 'Pcsk2', 'Pcsk2os1', 'Arhgap31', 'Arhgap33', 'Arhgap40', 'Arhgap6', 'Pdcd1lg2', 'Arhgef10', 'Pde10a', 'Arhgef18', 'Arhgef28', 'Arhgef9', 'Arid3a', 'Pdgfb', 'Arl13a', 'Arl4d', 'Pdlim1', 'Armc2', 'Armcx4', 'Arntl', 'Pdzph1', 'Arpp21', 'Peak1os', 'Pebp4', 'Peg3', 'Arsj', 'Per2', 'Arx', 'Arvcf', 'Asb2', 'Pfkfb4', 'Ascl4', 'Asic1', 'Asic4', 'Asna1', 'Pglyrp1', 'Phactr2', 'Phf21b', 'Atic', 'Phldb2', 'Atoh7', 'Pi15', 'Atp23', 'Atxn2l', 'Pitpnm3', 'Pitpnm2os2', 'Atxn7l2', 'Pitx2', 'Atxn7l3', 'Piwil4', 'Pkd1l2', 'Pkdcc', 'Pkhd1', 'Pkib', 'B130024G19Rik', 'Pla2g10', 'Pla2r1', 'Plat', 'Platr27', 'Platr8', 'Plbd1', 'B430010I23Rik', 'Plcb3', 'Pld5', 'B830042I05Rik', 'Plek2', 'Plekha6', 'BC017158', 'BC034090', 'Plekho2', 'BC037032', 'Plin3', 'Plk2', 'BC050972', 'BC051226', 'Plppr5', 'Bach2os', 'Plut', 'Bag3', 'Bahcc1', 'Baiap2', 'Baiap3', 'Bambi', 'Plxnc1', 'Pmepa1', 'Barx2', 'Pmepa1os', 'Bbox1', 'Bbs1', 'Bcam', 'Bcar3', 'Bcas1', 'Pnpla2', 'Pnrc1', 'Bcas3os1', 'Podnl1', 'Pogk', 'Poglut1', 'Poglut2', 'Pold2', 'Bcl2l2', 'Polr1c', 'Becn1', 'Begain', 'Bet1l', 'Bicd2', 'Pou3f4', 'Bloc1s2', 'Bmp5', 'Bmp7', 'Bmx', 'Bnip2', 'Boc', 'Ppip5k2', 'Ppp1r12c', 'Ppp1r13b', 'Brd1', 'Ppp1r15a', 'Ppp1r16b', 'Brd3os', 'Ppp1r1b', 'Ppp1r26', 'Brip1os', 'Ppp1r3a', 'Ppp1r3d', 'Ppp1r9b', 'Ppp2r2c', 'Bsx', 'Btbd11', 'Btbd3', 'Btg2', 'Bves', 'Prag1', 'Prcd', 'C030007H22Rik', 'Prdm1', 'C030018K13Rik', 'C030032O16Rik', 'C030034L19Rik', 'Prdm8', 'Prdx4', 'Prelp', 'C1galt1c1', 'Prickle1', 'Prkaa2', 'C1s1', 'Prkag2os1', 'C2', 'Prkcb', 'Prkcd', 'C230034O21Rik', 'Prkcz', 'C2cd4c', 'C2cd4d', 'C330002G04Rik', 'C330013E15Rik', 'C4b', 'C530025M09Rik', 'C5ar1', 'C5ar2', 'Prox2', 'C8g', 'Prpf31', 'Cab39l', 'Prr11', 'Prrc2b', 'Cacna1h', 'Cacna2d2', 'Prrt1', 'Prrt2', 'Prss27', 'Prss36', 'Prss41', 'Prss45', 'Caln1', 'Calml4', 'Camk2a', 'Camk2b', 'Psme2b', 'Capsl', 'Ptch2', 'Ptchd4', 'Car15', 'Car3', 'Car8', 'Ptgs1', 'Pth1r', 'Carmil2', 'Casp6', 'Ptpn7', 'Catsperb', 'Ptprh', 'Catspere1', 'Ptpro', 'Cbfa2t3', 'Purg', 'Pygm', 'Ccdc134', 'Ccdc152', 'Ccdc162', 'Rab11fip4', 'Ccdc171', 'Rab20', 'Rab26os', 'Ccdc22', 'Ccdc24', 'Rab33b', 'Ccdc34os', 'Ccdc38', 'Ccdc84', 'Ccdc85c', 'Ccdc86', 'Rad21l', 'Ccn1', 'Ralgapb', 'Ccn6', 'Ralyl', 'Rap1gap', 'Rapgef5', 'Cd164l2', 'Rasgrf1', 'Rasl11a', 'Rasl11b', 'Rassf2', 'Raver2', 'Rax', 'Rbis', 'Cd82', 'Rbm42', 'Cd96', 'Rbms2', 'Rbms3', 'Rbmxl1', 'Cdc42ep1', 'Rbpms', 'Cdc42ep4', 'Rbsn', 'Rcor2', 'Rcsd1', 'Cdh23', 'Recql5os1', 'Cdk10', 'Ceacam1', 'Rfxap', 'Cebpb', 'Rgl2', 'Rgs2', 'Cenpt', 'Rhcg', 'Rhd', 'Rhobtb1', 'Rhobtb2', 'Ribc2', 'Rif1', 'Rimbp2', 'Ces5a', 'Ripor2', 'Cfap100', 'Cfap161', 'Rmi1', 'Rmnd5a', 'Cfap46', 'Rnd1', 'Cfap69', 'Rnf121', 'Cfap77', 'Rnf122', 'Cfb', 'Cfp', 'Rnf152', 'Chd5', 'Rnf24', 'Rnf5', 'Chil1', 'Robo2', 'Rogdi', 'Ror1', 'Ror2', 'Chrac1', 'Chrdl1', 'Rpe65', 'Chst3', 'Ciart', 'Cib1', 'Cic', 'Rrp15', 'Rsf1os1', 'Rsf1os2', 'Rslcan18', 'Cklf', 'Ckmt2', 'Rspo2', 'Clcn6', 'Rubie', 'Rufy2', 'Rundc3a', 'Clec3b', 'Clic3', 'Ryr1', 'Clic6', 'S100pbp', 'S1pr5', 'Sall3', 'Samd12', 'Sap30bpos', 'Sap30l', 'Cmtr2', 'Pigm', 'Cndp2', 'Cnnm3', 'Scg5', 'Cntn2', 'Cntnap1', 'Scly', 'Scpep1os', 'Scrib', 'Col1a1', 'Col1a2', 'Scrt1', 'Scube1', 'Scrt2', 'Col25a1', 'Col4a3', 'Sdk2', 'Sds', 'Col9a2', 'Colec12', 'Sec14l5', 'Selenbp1', 'Coq10b', 'Coq6', 'Corin', 'Coro2b', 'Sema3c', 'Sema3b', 'Sema3d', 'Sema4a', 'Sema3f', 'Cox6a2', 'Sema5a', 'Sema5b', 'Sema7a', 'Sept4', 'Cpne6', 'Serpinb5', 'Serpinb9', 'Serpine1', 'Serpine3', 'Cr2', 'Crebzf', 'Creld2', 'Sez6l', 'Sf3b4', 'Sfmbt1', 'Crlf2', 'Sfrp1', 'Crocc', 'Sft2d2', 'Sfxn2', 'Sgcz', 'Crybg2', 'Crym', 'Sgsm1', 'Sh2b1', 'Csmd2', 'Sh3gl2', 'Sh3glb2', 'Cstdc2', 'Sh3rf2', 'Sh3rf3', 'Sh3tc2', 'Shank2', 'Ctif', 'Shh', 'Shisa3', 'Shisa6', 'Shmt1', 'Ctsh', 'Shmt2', 'Shprh', 'Shroom2', 'Sik1', 'Cutal', 'Sim2', 'Sipa1l1', 'Sipa1l3', 'Cxcr4', 'Sirt7', 'Six3', 'Six3os1', 'Cyb561', 'Cyb561d1', 'Skida1', 'Cyp2c50', 'Cyp2c55', 'Slc13a3', 'Cyp2c69', 'Slc13a5', 'Slc14a2', 'Slc15a1', 'Slc16a2', 'Cyp4f15', 'Slc17a9', 'Cyp51', 'Cyth1', 'Slc22a12', 'D030046N08Rik', 'Slc22a20', 'D030068K23Rik', 'Slc22a27', 'D130079A08Rik', 'Slc24a4', 'Slc25a19', 'D630003M21Rik', 'Slc25a29', 'Slc25a47', 'Slc26a1', 'Slc26a2', 'Dach1', 'Dancr', 'Slc2a5', 'Dapk1', 'Dapk2', 'Slc30a4', 'Dbil5', 'Dcc', 'Dcdc2a', 'Dcdc2b', 'Slc38a1', 'Slc39a1', 'Ddias', 'Slc45a3', 'Slc46a3', 'Ddx3y', 'Slc5a11', 'Slc5a7', 'Defb39', 'Defb40', 'Slc6a7', 'Slc7a2', 'Slc7a3', 'Deptor', 'Derl3', 'Dgat2', 'Slc9b1', 'Dgki', 'Dhcr24', 'Dhcr7', 'Dhrs3', 'Slitrk2', 'Dhx58os', 'Slx1b', 'Diaph3', 'Dicer1', 'Smad3', 'Dio2', 'Diras2', 'Disc1', 'Dixdc1', 'Dkk3', 'Dlg4', 'Smg8', 'Dlk1', 'Smim22', 'Dlx2', 'Dmp1', 'Dmrta2', 'Smyd1', 'Dnaic1', 'Dnaja4', 'Snhg3', 'Snhg4', 'Snhg9', 'Dnajc27', 'Dnajc30', 'Dnd1', 'Sntg1', 'Doc2a', 'Snx20', 'Dock10', 'Dock11', 'Snx25', 'Dock5', 'Snx30', 'Dok6', 'Dok7', 'Dolpp1', 'Sod3', 'Sorcs3', 'Sowaha', 'Sox13', 'Sox4', 'Drc1', 'Sp5', 'Dusp1', 'Dusp27', 'Dusp6', 'Spatc1l', 'Specc1l', 'Spef2', 'Dynlt1b', 'Spg20', 'Dzank1', 'Spin4', 'Spink2', 'E030042O20Rik', 'E230032D23Rik', 'E230034D01Rik', 'Spsb4', 'Sqle', 'Ece1', 'Echdc2', 'Srcap', 'Srcin1', 'Srd5a3', 'Edrf1', 'Eef1akmt3', 'Eef1akmt4', 'Ssbp3', 'Ssbp4', 'Efcab10', 'Ssc4d', 'Sspo', 'Efcc1', 'Efhd1', 'Efhd2', 'Efna2', 'St3gal2', 'Eftud2', 'St6gal2', 'Egfem1', 'Egfl6', 'St6galnac3', 'St6galnac5', 'Egln3', 'Egr1', 'St8sia3os', 'Ehd2', 'Ehd3', 'Stab2', 'Stambpl1', 'Stat4', 'Stimate', 'Elavl4', 'Elfn2', 'Stk32a', 'Elmo1', 'Elmo2', 'Elmod1', 'Stk39', 'Elovl2', 'Stom', 'Elovl6', 'Emc4', 'Eml2', 'Stx1b', 'Emsy', 'Emx2', 'Emx2os', 'Enkd1', 'Enkur', 'Sulf2', 'Enpp2', 'Sult1a1', 'Sult2a6', 'Enthd1', 'Entpd3', 'Susd4', 'Epha3', 'Epha5', 'Svil', 'Ephb3', 'Swap70', 'Syn3', 'Eps8l1', 'Syne4', 'Syngap1', 'Synj1', 'Ercc3', 'Ercc6l2', 'Syt3', 'Sytl5', 'Esrrb', 'Ess2', 'Tada1', 'Taf13', 'Etv4', 'Etv5', 'Taf7l', 'Eva1b', 'Tafa1', 'Exoc2', 'Extl1', 'Eya1', 'Eya4', 'F2', 'Tbc1d10c', 'F5', 'F630206G17Rik', 'Fads3', 'Fam126b', 'Fam163a', 'Fam163b', 'Fam177a', 'Fam181a', 'Tdrd3', 'Fam193b', 'Tek', 'Fam228a', 'Fam229a', 'Fam3c', 'Fam43b', 'Fam57b', 'Tex22', 'Fam92b', 'Tex9', 'Far1os', 'Fas', 'Tgfbr3', 'Tgfbr3l', 'Tgif1', 'Fbll1', 'Fbln1', 'Fbln5', 'Fbp1', 'Fbxl14', 'Fbxo15', 'Thoc3', 'Ticam2', 'Fbxo9', 'Fcgr2b', 'Fcgr3', 'Fcrls', 'Fdft1', 'Fem1a', 'Tle2', 'Tlnrd1', 'Fgf10', 'Fgf13', 'Fgf20', 'Fgf5', 'Fgf9', 'Fgfr4', 'Tmcc2', 'Fhad1', 'Fhl1', 'Tmeff1', 'Fibp', 'Tmem100', 'Filip1l', 'Firre', 'Tmem123', 'Tmem125', 'Tmem132b', 'Tmem140', 'Flrt1', 'Flrt2', 'Flt3l', 'Flvcr1', 'Fmo1', 'Fn3krp', 'Tmem173', 'Fndc5', 'Tmem181a', 'Tmem191c', 'Folr1', 'Folr2', 'Fos', 'Foxa1', 'Foxa3', 'Tmem204', 'Foxd1', 'Foxd2os', 'Foxg1', 'Tmem203', 'Tmem216', 'Tmem245', 'Frem1', 'Frmpd1os', 'Frmpd4', 'Tmem269', 'Frzb', 'Tmem37', 'Tmem44', 'Tmem50a', 'Fuca1', 'Fuca2', 'Tmem63a', 'Fut4', 'Tmem86a', 'Fzd10', 'Fzd10os', 'Fzd2', 'Fzd4', 'G0s2', 'G630016G05Rik', 'Tmprss6', 'G630093K05Rik', 'Gabra2', 'Gabra6', 'Tnfrsf19', 'Gabrr2', 'Tnfrsf26', 'Tnfsf11', 'Tnfsf12', 'Tnfsf4', 'Tnfsf8', 'Galnt10', 'Tnk2', 'Galnt2', 'Galnt5', 'Tnni3', 'Tns1', 'Gas1', 'Togaram2', 'Gas2l2', 'Tomm6os', 'Gata4', 'Gatc', 'Gatd3a', 'Tor1b', 'Tpd52l1', 'Gc', 'Gcnt1', 'Gcnt4', 'Gcnt7', 'Gdf10', 'Trac', 'Gdpd3', 'Gfm1', 'Ggnbp1', 'Trcg1', 'Trdn', 'Gigyf1', 'Triap1', 'Gins4', 'Gjb2', 'Gjc2', 'Gjc3', 'Gldnos', 'Gle1', 'Gli1', 'Glipr1l3', 'Glipr2', 'Glmp', 'Trim8', 'Glrb', 'Trip6', 'Glt8d2', 'Trmu', 'Trp63', 'Trpm1', 'Gnmt', 'Gnpda2', 'Gnpnat1', 'Trpv2', 'Got2', 'Tshr', 'Tsix', 'Gpatch4', 'Gpc3', 'Tspan4', 'Tspoap1', 'Gpr141', 'Gpr146', 'Gpr153', 'Gpr155', 'Ttc12', 'Gpr162', 'Gpr37', 'Ttc34', 'Gpr87', 'Gprasp2', 'Gprc5c', 'Gpt2', 'Gprin2', 'Gpx8', 'Gramd4', 'Tubal3', 'Grid2ip', 'Grifin', 'Grik4', 'Grik5', 'Grin3a', 'Grin3b', 'Grm4', 'Uap1', 'Gssos1', 'Ubash3b', 'Gstt2', 'Gstz1', 'Ube4bos1', 'Ubfd1', 'Ubqln4', 'Ubtd1', 'Ubxn11', 'Gyg', 'Uckl1os', 'Ucma', 'H1f0', 'Uggt2', 'H2-K1', 'Umad1', 'H2-T23', 'Unc13c', 'Unc80', 'Hal', 'Upf1', 'Hapln1', 'Hccs', 'Ush2a', 'Hdac4', 'Hdc', 'Hdhd5', 'Usp46os2', 'Heg1', 'Usp6nl', 'Hemk1', 'Uty', 'Uvssa', 'Herpud1', 'Hesx1', 'Hexa', 'Heyl', 'Vash1', 'Vasn', 'Vasp', 'Hgh1', 'Higd1b', 'Higd1c', 'Vip', 'Hist1h2ad', 'Vmn2r15', 'Vmn2r29', 'Hist1h4j', 'Hist2h2aa1', 'Hmcn1', 'Hmga1b', 'Hmgb2', 'Hmgcr', 'Hmgcs2', 'Vsig4', 'Hmgn5', 'Vstm4', 'Hnf4aos', 'Wars2', 'Hopx', 'Hoxa1', 'Hpcal1', 'Hpse', 'Hr', 'Hrh1', 'Wdr45', 'Hsd11b1', 'Wdr72', 'Wdr76', 'Wdr78', 'Wdr81', 'Hsd17b6', 'Hsf3', 'Hspa12a', 'Whamm', 'Wipf1', 'Wls', 'Wnt6', 'Wnt7a', 'Htr1f', 'Hyou1', 'Icosl', 'Id1', 'Ide', 'Idi1', 'Ier2', 'Yif1a', 'Ifi27', 'Ifi47', 'Yy2', 'Zap70', 'Igdcc3', 'Zbtb3', 'Igf2bp3', 'Igsf21', 'Zc3hav1', 'Il10rb', 'Zcwpw2', 'Il15', 'Il17c', 'Il1rapl2', 'Zfand2a', 'Il22ra1', 'Il31ra', 'Il33', 'Zfp110', 'Il6ra', 'Zfp157', 'Zfp160', 'Impdh2', 'Zfp217', 'Zfp263', 'Inhbc', 'Inka1', 'Zfp319', 'Zfp330', 'Zfp335', 'Insig1', 'Zfp335os', 'Insyn2a', 'Zfp385a', 'Zfp414', 'Iqcc', 'Iqch', 'Zfp451', 'Iqub', 'Irak4', 'Zfp513', 'Zfp516', 'Irf7', 'Zfp524', 'Irs2', 'Zfp536', 'Irx3', 'Irx5', 'Zfp592', 'Zfp629', 'Itgbl1', 'Zfp708', 'Itpka', 'Itpr1', 'Izumo4', 'Jag1', 'Zfp799', 'Zfp808', 'Zfp811', 'Jph2', 'Zfp85', 'Jpx', 'Zfp853', 'Jun', 'Junb', 'Kat2a', 'Zfp936', 'Kat8', 'Katnal2', 'Kbtbd12', 'Kcna2', 'Kcnc4', 'Zfp981', 'Kcng1', 'Kcng2', 'Kcng3', 'Kcng4', 'Zfp991', 'Kcnh3', 'Kcnip1', 'Kcnj3', 'Zic1', 'Zic2', 'Zic4', 'Zic3', 'Zim3', 'Kcnk5', 'Kcns3', 'Kcnt1', 'Kctd12b', 'Kctd17', 'Kdm5d', 'Kdm8', 'Khdrbs3', 'Kif13b', 'Kif19a', 'Kif3c', 'Kifc5b', 'Kirrel2', 'Kiss1', 'Klc1', 'Klc2', 'Klhdc7a', 'Klhl10', 'Klhl25', 'Klra8', 'Klre1', 'Krt8', 'Ky', 'L3hypdh', 'L3mbtl4', 'Lamb3', 'Lao1', 'Lars2', 'Lca5l', 'Lck', 'Lctl', 'Ldhc', 'Ldlr', 'Lenep', 'Leprot', 'Lgals12', 'Lgr6', 'Lgsn', 'Lhfpl2', 'Lhx2', 'Lipo3', 'Lmo3', 'Lmod1', 'Lonrf2', 'Loxhd1', 'Loxl2', 'Lpar1', 'Lpgat1', 'Lpin2']

subregional_model2.fit(X_uni_to_PRJNA779749, codes)

joblib.dump(
    subregional_model2,
    f"models/svm/best-xgboost-subregional_PRJNA779749_astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle",
)

print(X_uni_to_PRJNA779749.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist())

['Lrrc8a', 'Ltbp1', 'Malat1', 'Map3k5', 'Mbp', 'Mdga2', 'Mdh1', '2900097C17Rik', 'Medag', 'Meis2', '4833423E24Rik', 'Mfge8', 'Mfsd2a', '4930447C04Rik', 'Mobp', 'Msi2', 'Msmo1', 'Mt1', 'Mt2', 'Mt3', 'Myoc', 'Nbl1', 'Ndn', 'Ndufa3', 'Nebl', 'Nefl', 'AC149090.1', 'Nhs', 'AY036118', 'Nnat', 'Acot4', 'Nr2f2', 'Nrarp', 'Nrp1', 'Nrsn1', 'Nrxn3', 'Adcy8', 'Ntsr2', 'Nupr1', 'Agt', 'Ank3', 'Ankfn1', 'Anln', 'Aplp1', 'Apoe', 'App', 'Aqp4', 'Arf3', 'Pcsk1n', 'Pde10a', 'Arhgef28', 'Pde7b', 'Arid1a', 'Pdgfd', 'Pdxp', 'Pdzph1', 'Arpp21', 'Peg3', 'Atp1a2', 'Atp1b2', 'Atp5g1', 'Atp5g2', 'Atp6v0c', 'Atpif1', 'Pitx2', 'Avp', 'Pkib', 'Pla2g10', 'B230323A14Rik', 'Basp1', 'Bc1', 'Ppia', 'Brinp3', 'Bsg', 'Prdx1', 'Camk2a', 'Camk2n1', 'Ptprd', 'Ralyl', 'Cd24a', 'Cd81', 'Cd9', 'Resp18', 'Cdk8', 'Cebpb', 'Rgs19', 'Rmst', 'Rnf165', 'Chchd10', 'Ckb', 'Rtn1', 'Rundc3a', 'S100a10', 'Samd12', 'Cmss1', 'Sap30bpos', 'Cntn1', 'Scg5', 'Cops9', 'Selenop', 'Sema3c', 'Cox4i1', 'Cox6c', 'Cox7a2', 'Cox8a', 'Cpe', 'Cped1', 'Sept4', 'Sept7', 'Sfrp1', 'Sgip1', 'Crym', 'Cst3', 'Ctnna2', 'Shisa6', 'Six3', 'Cycs', 'Cyfip2', 'Slc17a6', 'Dbp', 'Dcc', 'Slc6a11', 'Slco2a1', 'Slit2', 'Dlg2', 'Snhg3', 'Son', 'Sorbs1', 'Dpp10', 'Sparcl1', 'Dync1i1', 'Dynll1', 'Spock1', 'Spock3', 'Ednrb', 'Eef1d', 'Eef1g', 'Ssbp4', 'Egfem1', 'Eif1', 'Elfn2', 'Eno1', 'Sulf2', 'Swi5', 'Fabp5', 'Fabp7', 'Tceal9', 'Tenm1', 'Far1os', 'Fbln5', 'Thrsp', 'Timp3', 'Fgfr3', 'Fmo1', 'Fos', 'Fosb', 'Foxg1', 'Tmem262', 'Fth1', 'Tmsb4x', 'Gadd45g', 'Gas5', 'Gfap', 'Tril', 'Gjc3', 'Glul', 'Gnas', 'Trpm3', 'Tsc22d3', 'Tsix', 'Gpc5', 'Gpc6', 'Tspan7', 'Ttc3', 'Gpx1', 'Tubb2a', 'Gria1', 'Gria2', 'Uba52', 'Uckl1os', 'H3f3b', 'Uqcr11', 'Uqcrq', 'Usp29', 'Hes5', 'Vim', 'Hmgcs1', 'Hspa8', 'Xist', 'Id2', 'Ywhaz', 'Igf1r', 'Igf2', 'Iqub', 'Irak2', 'Itih3', 'Itm2c', 'Itpkb', 'Jaml', 'Jun', 'Zfp981', 'Kcnip4', 'Kcns2', 'Klc1', 'Klra8', 'Lars2', 'Ldhb', 'Lgals3', 'Lgi1', 'Lhfp', 'Lix1']

/opt/python/3.8.8/lib/python3.8/site-packages/sklearn/svm/_base.py:299: ConvergenceWarning:

Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.

Logit

# Generate labels for whole hypothalamus reference dataset
predictions_logit_PRJNA779749 = subregional_model.predict(df_eval2)
adata_eval2.obs["predict_logit_subregion"] = adata_eval2.obs["condit"]
adata_eval2.obs["predict_logit_subregion"] = predictions_logit_PRJNA779749
adata_eval2.obs["predict_logit_subregion"] = pd.Categorical(
    values=adata_eval2.obs["predict_logit_subregion"].astype(str),
    categories=natsorted(
        map(str, np.unique(adata_eval2.obs["predict_logit_subregion"].astype(str)))
    ),
)

PRJNA779749_regulons.obs["predict_logit_subregion"] = adata_eval2.obs["predict_logit_subregion"]

adata_eval2.obsm["X_umap_subfunct"] = adata_eval2.obsm["X_umap"]

reducer_PRJNA779749 = UMAP(
    n_neighbors=ks,
    n_epochs=1000,
    init="spectral",
    learning_rate=0.1,
    min_dist=min_dist,
    spread=spread,
    repulsion_strength=2.0,
    negative_sample_rate=negative_sample_rate,
    densmap=True,
    n_components=2,
    random_state=reseed,
    verbose=False,
)
# reducer_PRJNA779749.fit(
#     adata_eval2.obsm["X_aucell"], adata_eval2.obs["predict_logit_subregion"].astype(str)
# )

reducer_PRJNA779749.fit(
    adata_eval2.to_df().loc[:, X_uni_to_PRJNA779749.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist()], adata_eval2.obs["predict_logit_subregion"].astype(str)
)

UMAP(densmap=True, learning_rate=0.1, min_dist=1.0, n_epochs=1000, n_neighbors=20, negative_sample_rate=10, random_state=42, repulsion_strength=2.0, spread=2, tqdm_kwds={'bar_format': '{desc}: {percentage:3.0f}%| {bar} {n_fmt}/{total_fmt} [{elapsed}]', 'desc': 'Epochs completed', 'disable': True})

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# adata_eval2.obsm["X_umap_logit"] = reducer_PRJNA779749.transform(adata_eval2.obsm["X_aucell"])
adata_eval2.obsm["X_umap_logit"] = reducer_PRJNA779749.transform(adata_eval2.to_df().loc[:, X_uni_to_PRJNA779749.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist()])
PRJNA779749_regulons.obsm["X_umap_logit"] = adata_eval2.obsm["X_umap_logit"]

adata_eval2.write(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA779749-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)
adata_eval2.write_loom(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA779749-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.loom"
)
pd.DataFrame(adata_eval2.obsm["X_umap_logit"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA779749-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-logit.tsv",
    sep="\t",
    header=True,
)
pd.DataFrame(adata_eval2.obsm["X_umap_subfunct"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/PRJNA779749-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-subfunct.tsv",
    sep="\t",
    header=True,
)

The loom file will lack these fields:
{'X_pca', 'ora_pvals', 'X_aucell', 'X_umap', 'ora_estimate', 'X_umap_logit', 'X_pacmap', 'X_umap_subfunct'}
Use write_obsm_varm=True to export multi-dimensional annotations

save_PRJNA779749 = f"-best_logit-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pdf"

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=["predict_logit_subregion", "k_tree"],
    title=f"UMAP supervised subfunction: PRJNA779749 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save=save_PRJNA779749)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-best_logit-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=["predict_logit_subregion", "k_tree"],
    title=f"UMAP supervised density logit prediction: PRJNA779749 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-predict_logit_subregion" + save_PRJNA779749,
)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-predict_logit_subregion-best_logit-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

XGBoost

# Generate labels for whole hypothalamus reference dataset
predictions_xgboost_PRJNA779749 = subregional_model2.predict(df_eval2)
adata_eval2.obs["predict_xgboost_subregion"] = adata_eval2.obs["condit"]
adata_eval2.obs["predict_xgboost_subregion"] = predictions_xgboost_PRJNA779749
adata_eval2.obs["predict_xgboost_subregion"] = pd.Categorical(
    values=adata_eval2.obs["predict_xgboost_subregion"].astype(str),
    categories=natsorted(
        map(str, np.unique(adata_eval2.obs["predict_xgboost_subregion"].astype(str)))
    ),
)

PRJNA779749_regulons.obs["predict_xgboost_subregion"] = adata_eval2.obs["predict_xgboost_subregion"]

reducer_PRJNA779749 = UMAP(
    n_neighbors=ks,
    n_epochs=1000,
    init="spectral",
    learning_rate=0.1,
    min_dist=min_dist,
    spread=spread,
    repulsion_strength=2.0,
    negative_sample_rate=negative_sample_rate,
    densmap=True,
    n_components=2,
    random_state=reseed,
    verbose=False,
)
# reducer_PRJNA779749.fit(
#     adata_eval2.obsm["X_aucell"],
#     adata_eval2.obs["predict_xgboost_subregion"].astype(str)
# )

reducer_PRJNA779749.fit(
    adata_eval2.to_df().loc[:, X_uni_to_PRJNA779749.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist()], adata_eval2.obs["predict_xgboost_subregion"].astype(str)
)

UMAP(densmap=True, learning_rate=0.1, min_dist=1.0, n_epochs=1000, n_neighbors=20, negative_sample_rate=10, random_state=42, repulsion_strength=2.0, spread=2, tqdm_kwds={'bar_format': '{desc}: {percentage:3.0f}%| {bar} {n_fmt}/{total_fmt} [{elapsed}]', 'desc': 'Epochs completed', 'disable': True})

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# adata_eval2.obsm["X_umap"] = reducer_PRJNA779749.transform(adata_eval2.obsm["X_aucell"])
adata_eval2.obsm["X_umap"] = reducer_PRJNA779749.transform(adata_eval2.to_df().loc[:, X_uni_to_PRJNA779749.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist()])
PRJNA779749_regulons.obsm["X_umap_xgboost"] = adata_eval2.obsm["X_umap"]

adata_eval2.write(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA779749-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)
adata_eval2.write_loom(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA779749-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.loom"
)
pd.DataFrame(adata_eval2.obsm["X_umap"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA779749-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-xgboost.tsv",
    sep="\t",
    header=True,
)

The loom file will lack these fields:
{'X_pca', 'ora_pvals', 'X_aucell', 'X_umap', 'ora_estimate', 'X_umap_logit', 'X_pacmap', 'X_umap_subfunct'}
Use write_obsm_varm=True to export multi-dimensional annotations

save_PRJNA779749 = f"-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pdf"

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=["predict_xgboost_subregion", "k_tree"],
    title=f"UMAP supervised subfunction: PRJNA779749 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save=save_PRJNA779749,
)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.umap(
    adata_eval2,
    color=["predict_xgboost_subregion"],
    title=f"UMAP: supervised density XGBoost, PRJNA779749 \n(amb.FPR={cb_fpr}, k={k}, MSP={connectivity_model}, \nmetric={metric}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-predict_xgboost_subregion" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-predict_xgboost_subregion-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

npepdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in npep if g in adata_eval2.var_names])]
npepdf.describe()
npepcols = [col for col in npepdf.columns if npepdf[col].quantile(0.985) > 1]

for gene in npepcols:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        vmax="p98.5",
        na_color="#9A9FB080",
        save="-npep-" + gene + save_PRJNA779749,
)

nprdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in npr if g in adata_eval2.var_names])]
nprdf.describe()

	Adcyap1r1	Avpr1a	Calcr	Calcrl	Cckar	Cckbr	Cntfr	Crhr1	Crhr2	Esr1	...	Sstr1	Sstr2	Sstr3	Tacr1	Tacr3	Trhr	Trhr2	Tshr	Vipr1	Vipr2
count	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	...	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.0	6199.000000	6199.000000	6199.000000
mean	0.268269	0.000968	0.005646	0.004839	0.000161	0.002097	0.199387	0.001452	0.000645	0.012260	...	0.001129	0.005323	0.001452	0.007098	0.003872	0.001774	0.0	0.143733	0.000645	0.005162
std	0.651514	0.031099	0.140759	0.087869	0.012701	0.052330	0.489575	0.038079	0.025396	0.272153	...	0.033588	0.081158	0.038079	0.135435	0.069464	0.045764	0.0	0.577760	0.025396	0.168433
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000
75%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000
max	9.000000	1.000000	6.000000	3.000000	1.000000	2.000000	6.000000	1.000000	1.000000	16.000000	...	1.000000	2.000000	1.000000	7.000000	2.000000	2.000000	0.0	7.000000	1.000000	11.000000

8 rows × 67 columns

nprdf.quantile(q=[0.5, 0.9, 0.985, 1.0], axis=0)

	Adcyap1r1	Avpr1a	Calcr	Calcrl	Cckar	Cckbr	Cntfr	Crhr1	Crhr2	Esr1	...	Sstr1	Sstr2	Sstr3	Tacr1	Tacr3	Trhr	Trhr2	Tshr	Vipr1	Vipr2
0.500	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.900	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.985	3.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0
1.000	9.0	1.0	6.0	3.0	1.0	2.0	6.0	1.0	1.0	16.0	...	1.0	2.0	1.0	7.0	2.0	2.0	0.0	7.0	1.0	11.0

4 rows × 67 columns

nprcols = [col for col in nprdf.columns if nprdf[col].quantile(0.985) > 1]
nprcols3 = [col for col in nprdf.columns if col not in nprcols and nprdf[col].max() == 1]
nprcols2 = [col for col in nprdf.columns if col not in nprcols and col not in nprcols3]
print(nprcols)
print(nprcols2)
print(nprcols3)

['Adcyap1r1', 'Cntfr', 'Ghr', 'Igf1r', 'Insr', 'Nmur2', 'Ntrk2', 'Ntsr2', 'Tshr']
['Calcr', 'Calcrl', 'Cckbr', 'Esr1', 'Galr1', 'Gpr83', 'Gpr149', 'Grpr', 'Hcrtr2', 'Kiss1r', 'Lepr', 'Nmbr', 'Nmur1', 'Npffr1', 'Npr2', 'Npr3', 'Npsr1', 'Npy1r', 'Npy2r', 'Ntsr1', 'Oprd1', 'Oprk1', 'Oprl1', 'Oprm1', 'Prlr', 'Prokr2', 'Qrfpr', 'Rxfp2', 'Sstr2', 'Tacr1', 'Tacr3', 'Trhr', 'Trhr2', 'Vipr2']
['Avpr1a', 'Cckar', 'Crhr1', 'Crhr2', 'Galr2', 'Galr3', 'Ghsr', 'Glp1r', 'Gpr55', 'Hcrtr1', 'Insrr', 'Mc1r', 'Mc3r', 'Mc4r', 'Mchr1', 'Npffr2', 'Npr1', 'Npy5r', 'Oxtr', 'Prlhr', 'Rxfp1', 'Sstr1', 'Sstr3', 'Vipr1']

sc.pl.umap(
    adata_eval2,
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nprcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-npr2" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr2-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nprcols3,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    na_color="#9A9FB080",
    save="-npr3" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr3-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

nmrdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in nmr if g in adata_eval2.var_names])]
nmrdf.describe()

	Adra1a	Adra1b	Adra1d	Adra2a	Adra2b	Adra2c	Adrb1	Adrb2	Adrb3	Adrm1	...	P2rx7	P2ry1	P2ry2	P2ry6	P2ry12	P2ry13	P2ry14	Ryr1	Ryr2	Ryr3
count	6199.000000	6199.000000	6199.000000	6199.000000	6199.0	6199.000000	6199.000000	6199.000000	6199.000000	6199.00000	...	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000
mean	0.090337	0.022262	0.000161	0.035490	0.0	0.000645	0.024520	0.000645	0.000968	0.00613	...	0.077271	0.010970	0.000968	0.000807	0.079690	0.001129	0.297951	0.001613	0.047911	0.026779
std	0.374099	0.165092	0.012701	0.197676	0.0	0.025396	0.161807	0.025396	0.031099	0.07806	...	0.291868	0.108715	0.031099	0.028391	0.351237	0.033588	0.719063	0.040135	0.718848	0.574206
min	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.00000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.00000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.00000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.00000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	7.000000	3.000000	1.000000	2.000000	0.0	1.000000	2.000000	1.000000	1.000000	1.00000	...	3.000000	3.000000	1.000000	1.000000	4.000000	1.000000	10.000000	1.000000	38.000000	31.000000

8 rows × 139 columns

nmrcols = [col for col in nmrdf.columns if nmrdf[col].quantile(0.985) > 1]
nmrcols3 = [col for col in nmrdf.columns if col not in nmrcols and nmrdf[col].max() == 1]
nmrcols2 = [col for col in nmrdf.columns if col not in nmrcols and col not in nmrcols3]
print(nmrcols)
print(nmrcols2)
print(nmrcols3)

['Gria1', 'Gria2', 'Grid1', 'Grid2', 'Grik2', 'Grik3', 'Grik4', 'Grik5', 'Grin2b', 'Grin2c', 'Grin3a', 'Grm3', 'Grm7', 'Gabra2', 'Gabrb1', 'Gabrb3', 'Gabrg1', 'Gabrg3', 'Gabbr1', 'Gabbr2', 'Gnas', 'Gnai2', 'Gnao1', 'Gnaq', 'Gna12', 'Gna13', 'Gnb1', 'Gng12', 'P2ry14']
['Adra1a', 'Adra1b', 'Adra2a', 'Adra2b', 'Adrb1', 'Adora1', 'Adora2b', 'Adora3', 'Chrm1', 'Chrm2', 'Chrm3', 'Chrna3', 'Chrna4', 'Chrna7', 'Chrna9', 'Chrnb1', 'Gria3', 'Gria4', 'Grik1', 'Grin1', 'Grin2a', 'Grin2d', 'Grm1', 'Grm5', 'Grm4', 'Grm8', 'Gabra1', 'Gabra3', 'Gabra4', 'Gabra5', 'Gabrb2', 'Gabrg2', 'Gabre', 'Gabrp', 'Gabrr1', 'Gabrr2', 'Gabrr3', 'Drd3', 'Drd4', 'Htr1f', 'Htr2b', 'Htr2c', 'Htr3b', 'Htr4', 'Htr5b', 'Htr6', 'Htr7', 'Gnai1', 'Gnai3', 'Gna11', 'Gnal', 'Gnb2', 'Gnb4', 'Gnb5', 'Gng2', 'Gng3', 'Gng4', 'Gng5', 'Gng7', 'Gng10', 'Gng11', 'P2rx1', 'P2rx4', 'P2rx6', 'P2rx7', 'P2ry1', 'P2ry12', 'Ryr2', 'Ryr3']
['Adra1d', 'Adra2c', 'Adrb2', 'Adrb3', 'Adrm1', 'Adora2a', 'Chrm4', 'Chrm5', 'Chrna1', 'Chrna2', 'Chrna5', 'Chrna6', 'Chrna10', 'Chrnb2', 'Chrnb3', 'Chrng', 'Grin3b', 'Grm2', 'Gabra6', 'Gabrd', 'Gabrq', 'Drd1', 'Drd2', 'Drd5', 'Htr1a', 'Htr1b', 'Htr1d', 'Htr2a', 'Htr3a', 'Htr5a', 'Gnb3', 'Gng8', 'Gng13', 'Gngt2', 'P2rx2', 'P2rx3', 'P2rx5', 'P2ry2', 'P2ry6', 'P2ry13', 'Ryr1']

sc.pl.umap(
    adata_eval2,
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nmrcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-nmr2" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr2-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nmrcols3,
    frameon=False,
    show=True,
    size=40,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    vmin=0.0,
    na_color="#9A9FB080",
    save="-nmr3" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr3-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

adddf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in genes_embed if g in adata_eval2.var_names])]
adddf.describe()

	Abcd1	Abcd2	Abcd3	Acaa2	Acox1	Agt	Aldh1a1	Aldh1l1	Aldoc	Apoe	...	Slc4a4	Slc6a11	Slc7a10	Slc38a1	Slit1	Slit2	Sox9	Tafa1	Tkt	Trpv1
count	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	...	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.00000	6199.000000	6199.000000	6199.0
mean	0.015970	0.188579	0.213422	0.044523	0.187934	1.524923	0.480561	0.178900	0.468463	16.582191	...	4.516535	2.335377	0.769802	0.295854	0.014518	1.035328	0.16051	0.254719	0.165511	0.0
std	0.131648	0.498554	0.511034	0.220633	0.484356	1.938881	1.217902	0.468188	0.830368	14.464943	...	6.463615	2.706933	1.068522	1.062230	0.205087	3.198228	0.46311	1.142395	0.424368	0.0
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.0
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.000000	...	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.0
50%	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	13.000000	...	2.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.0
75%	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	1.000000	0.000000	1.000000	21.000000	...	5.000000	3.000000	1.000000	0.000000	0.000000	1.000000	0.00000	0.000000	0.000000	0.0
max	3.000000	5.000000	5.000000	3.000000	5.000000	20.000000	26.000000	5.000000	12.000000	270.000000	...	96.000000	28.000000	12.000000	22.000000	6.000000	55.000000	9.00000	43.000000	3.000000	0.0

8 rows × 78 columns

agcols = [col for col in adddf.columns if adddf[col].quantile(0.985) > 1]
agcols3 = [col for col in adddf.columns if col not in agcols and adddf[col].max() == 1]
agcols2 = [col for col in adddf.columns if col not in agcols and col not in agcols3]
print(agcols)
print(agcols2)
print(agcols3)

['Abcd2', 'Abcd3', 'Acox1', 'Agt', 'Aldh1a1', 'Aldh1l1', 'Aldoc', 'Apoe', 'Aqp4', 'Ckb', 'Cst3', 'Dcc', 'Egfr', 'Eno1', 'Fgfr3', 'Fth1', 'Gfap', 'Gja1', 'Glul', 'Hacd2', 'Hepacam', 'Htra1', 'Lpcat3', 'Mfn1', 'Mgll', 'Ndrg2', 'Nfia', 'Opa1', 'Pex13', 'Pex14', 'Pla2g7', 'Plcb1', 'Pygb', 'S100a6', 'S100b', 'Scd2', 'Sgcd', 'Slc1a2', 'Slc1a3', 'Slc4a4', 'Slc6a11', 'Slc7a10', 'Slc38a1', 'Slit2', 'Sox9', 'Tafa1', 'Tkt']
['Abcd1', 'Acaa2', 'Cnr1', 'Dagla', 'Daglb', 'Decr2', 'Dnm1', 'Ech1', 'Faah', 'Fis1', 'Fos', 'Ftl1', 'Gli1', 'Hadhb', 'Lcat', 'Lxn', 'Mfn2', 'Mief1', 'Napepld', 'Otp', 'Pex1', 'Pex10', 'Pex12', 'Pex16', 'Pex2', 'Pex26', 'Pex3', 'Pex6', 'Slit1', 'Trpv1']
['Cnr2']

sc.pl.umap(
    adata_eval2,
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=agcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-adgen2" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen2-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=agcols3,
    frameon=False,
    show=True,
    size=40,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    vmin=0.0,
    na_color="#9A9FB080",
    save="-adgen3" + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen3-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

tfdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in tf_genes if g in adata_eval2.var_names])]
tfdf.describe()

	Aff3	Alx1	Arid1a	Arid1b	Arid2	Arid3a	Arid3b	Arid3c	Arid4a	Arid4b	...	Caskin1	Gtf3c4	Gcdh	BC024139	Zfp398	Tcfl5	A630089N07Rik	Phf8	Zhx3	Zfp82
count	6199.000000	6199.0	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	...	6199.000000	6199.000000	6199.000000	6199.000000	6199.000000	6199.0	6199.000000	6199.000000	6199.000000	6199.000000
mean	0.154702	0.0	0.197774	0.415874	0.230037	0.004194	0.020326	0.000484	0.230682	0.310373	...	0.211324	0.008388	0.031134	0.000645	0.162930	0.0	0.133570	0.098242	0.541055	0.012421
std	0.624941	0.0	0.513693	0.891851	0.575106	0.071731	0.149991	0.021995	0.568355	0.707773	...	0.511116	0.096372	0.177371	0.031107	0.470395	0.0	0.449697	0.348595	0.973785	0.112213
min	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.0	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	1.000000	0.000000
max	14.000000	0.0	5.000000	12.000000	8.000000	2.000000	3.000000	1.000000	9.000000	14.000000	...	7.000000	2.000000	2.000000	2.000000	8.000000	0.0	8.000000	4.000000	15.000000	2.000000

8 rows × 1374 columns

tfdf.quantile(q=[0.5, 0.9, 0.985, 1.0], axis=0)

	Aff3	Alx1	Arid1a	Arid1b	Arid2	Arid3a	Arid3b	Arid3c	Arid4a	Arid4b	...	Caskin1	Gtf3c4	Gcdh	BC024139	Zfp398	Tcfl5	A630089N07Rik	Phf8	Zhx3	Zfp82
0.500	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.900	1.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	1.0	1.0	...	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	2.0	0.0
0.985	2.0	0.0	2.0	4.0	2.0	0.0	1.0	0.0	2.0	3.0	...	2.0	0.0	1.0	0.0	2.0	0.0	2.0	1.0	4.0	0.0
1.000	14.0	0.0	5.0	12.0	8.0	2.0	3.0	1.0	9.0	14.0	...	7.0	2.0	2.0	2.0	8.0	0.0	8.0	4.0	15.0	2.0

4 rows × 1374 columns

tfcols = [col for col in tfdf.columns if tfdf[col].quantile(0.9) > 3]
tfcols3 = [col for col in tfdf.columns if col not in tfcols and tfdf[col].max() == 1]
tfcols2 = [col for col in tfdf.columns if col not in tfcols and tfdf[col].max() > 1]

print(tfcols)
print(tfcols2)
print(tfcols3)

['Ddx5', 'Rora', 'Rorb', 'Sox6', 'Son', 'Hdac8', 'Npas3', 'Nfia', 'Ptch1', 'Pbx1', 'Syne1', 'Tcf4', 'Trps1', 'Zbtb20', 'Ank2']
['Aff3', 'Arid1a', 'Arid1b', 'Arid2', 'Arid3a', 'Arid3b', 'Arid4a', 'Arid4b', 'Arid5b', 'Bcl3', 'Bcl6', 'Bdp1', 'Btaf1', 'Bcor', 'Brf1', 'Bach1', 'Bach2', 'Barx2', 'Bmi1', 'Ctbp1', 'Ctbp2', 'C1d', 'Cebpb', 'Cebpd', 'Cebpg', 'Cebpz', 'Ctcf', 'Cnot3', 'Cops2', 'Crebbp', 'Dbp', 'Dpf2', 'Ddx3x', 'Ddit3', 'Drap1', 'Ep300', 'Ets1', 'Ets2', 'E2f3', 'E2f4', 'E2f5', 'E2f6', 'E4f1', 'Elf1', 'Elf2', 'Elk1', 'Elk3', 'Elk4', 'Eaf2', 'Eid1', 'Erf', 'Ewsr1', 'Fos', 'Daxx', 'Fezf2', 'Fli1', 'Gabpa', 'Gatad2b', 'Gli1', 'Gli2', 'Gli3', 'Glis2', 'Herc1', 'Ikzf1', 'Isl1', 'Irx3', 'Jdp2', 'Kat2a', 'Kat2b', 'Kat5', 'Kat6a', 'Klf10', 'Klf12', 'Klf13', 'Klf15', 'Klf16', 'Klf4', 'Klf5', 'Klf7', 'Klf9', 'Kcnip3', 'Lpp', 'Lhx1', 'Lhx2', 'Mxd1', 'Mxi1', 'Mlx', 'Mecom', 'Mybbp1a', 'Mycbp', 'Maz', 'Max', 'Meis1', 'Meis2', 'Mdfi', 'Nkx2-1', 'Nkx6-2', 'Nab2', 'Paxip1', 'Pou2f1', 'Pou2f2', 'Pou3f2', 'Pou3f4', 'Pou6f1', 'Patz1', 'Pknox2', 'Pknox1', 'Rab11fip3', 'Rbak', 'Rest', 'Rcor2', 'Rybp', 'Rrn3', 'Arhgap35', 'Sertad2', 'Smyd1', 'Smyd3', 'Setdb1', 'Skil', 'Smad3', 'Smad5', 'Smad7', 'Smad9', 'Snw1', 'Spen', 'Sox1', 'Sox10', 'Sox11', 'Sox13', 'Sox14', 'Sox2', 'Sox21', 'Sox4', 'Sox5', 'Sox7', 'Sox8', 'Sox9', 'Sub1', 'Smarcal1', 'Smarca1', 'Smarca2', 'Smarca4', 'Smarca5', 'Smarcb1', 'Smarcc1', 'Smarcd1', 'Smarcd2', 'Smarcd3', 'Smarce1', 'Smarcad1', 'Tbx2', 'Tbx3', 'Tbpl1', 'Tmf1', 'Taf1', 'Taf11', 'Taf12', 'Taf2', 'Taf3', 'Taf4b', 'Taf6', 'Taf7l', 'Taf7', 'Taf8', 'Taf1a', 'Taf1c', 'Tead1', 'Tead3', 'Traf4', 'Tsc22d1', 'Snrpc', 'Wdr5', 'Wwtr1', 'Wwox', 'Xbp1', 'Ybx1', 'Yy1', 'Ascc1', 'Atf2', 'Atf4', 'Atf5', 'Atf6', 'Atf7ip', 'Atf7', 'Ar', 'Aatf', 'Arnt2', 'Arnt', 'Arntl', 'Ahr', 'Atr', 'Atm', 'Atoh8', 'Relb', 'Bhlhe40', 'Bhlhe41', 'Blzf1', 'Bmp7', 'Bin1', 'Bptf', 'Brd4', 'Brd7', 'Abl1', 'Creb1', 'Creb3', 'Creb3l1', 'Crem', 'Camta2', 'Cic', 'Ctnnb1', 'Cdc5l', 'Creg1', 'Clock', 'Ciita', 'Cbfa2t3', 'Cux1', 'Cux2', 'Dmtf1', 'Ccnt1', 'Ccnt2', 'Cdk7', 'Cdk8', 'Cdk9', 'Cdkn1a', 'Ddb1', 'Ddb2', 'Dcp1a', 'Dtx1', 'Ebf1', 'Eed', 'Emx2', 'Epas1', 'En1', 'Ezh1', 'Ezh2', 'Esr1', 'Esr2', 'Esrra', 'Esrrb', 'Esrrg', 'Etv1', 'Etv3', 'Etv4', 'Etv5', 'Foxa1', 'Foxd1', 'Foxg1', 'Foxj2', 'Foxk1', 'Foxo1', 'Foxo3', 'Foxo4', 'Foxo6', 'Foxp1', 'Foxp2', 'Foxp4', 'Fosl1', 'Fosl2', 'Fus', 'Gbx2', 'Gtf2h1', 'Gtf2h2', 'Gtf2h4', 'Gtf2ird1', 'Gtf2i', 'Gtf2b', 'Gtf3a', 'Gtf3c1', 'Gmeb1', 'Gmeb2', 'Grhl1', 'Hes5', 'Hes6', 'Hes7', 'Hey1', 'Hsf1', 'Hsbp1', 'Hsf4', 'Hltf', 'Hlf', 'Hnrnpab', 'Hnrnpc', 'Hnrnpdl', 'Hnrnph2', 'Hnrnpk', 'Hnrnpl', 'Hnrnpll', 'Hnrnpu', 'Hmga1', 'Hmgb1', 'Hmgb2', 'Hmgn1', 'Hmgn3', 'Hdac1', 'Hdac10', 'Hdac2', 'Hdac3', 'Hdac4', 'Hdac5', 'Hdac6', 'Hdac7', 'Hdac9', 'Hcfc1', 'Hivep1', 'Hivep2', 'Hif1a', 'Id1', 'Id2', 'Id3', 'Id4', 'Itgb3bp', 'Irf1', 'Irf2', 'Irf3', 'Irf5', 'Irf9', 'Jade1', 'Junb', 'Jund', 'Lztfl1', 'Lcor', 'Lef1', 'Kdm5a', 'Kdm5b', 'Kdm5c', 'Kmt2a', 'Maml1', 'Maml3', 'Mnt', 'Med1', 'Med12', 'Med14', 'Med15', 'Med17', 'Med21', 'Med23', 'Med24', 'Med6', 'Mnat1', 'Mn1', 'Mtf1', 'Mtf2', 'Mta1', 'Mta2', 'Mecp2', 'Mbd1', 'Mbd2', 'Mitf', 'Mapk14', 'Mapk3', 'Mapk7', 'Myef2', 'Myt1', 'Myt1l', 'Mybl1', 'Mybl2', 'Mzf1', 'Mllt1', 'Mllt11', 'Mllt3', 'Mllt6', 'Mef2a', 'Mef2d', 'Nrl', 'Nrg1', 'Neurod2', 'Npas1', 'Npas2', 'Notch1', 'Notch2', 'Notch3', 'Nfic', 'Nfix', 'Nfat5', 'Nfatc1', 'Nfatc2', 'Nfatc3', 'Nfatc4', 'Nfkb1', 'Nfkb2', 'Nfkbia', 'Nfkbiz', 'Nfe2l3', 'Nfe2l1', 'Nfil3', 'Nufip1', 'Ncor1', 'Ncor2', 'Ncoa1', 'Ncoa2', 'Ncoa3', 'Ncoa4', 'Ncoa5', 'Ncoa6', 'Ncoa7', 'Nrip1', 'Nrip3', 'Nr1d1', 'Nr1d2', 'Nr1h2', 'Nr1h3', 'Nr1i2', 'Nr1i3', 'Nr2c1', 'Nr2c2', 'Nr2e1', 'Nr2f1', 'Nr2f2', 'Nr2f6', 'Nr3c1', 'Nr3c2', 'Nr4a1', 'Nr4a2', 'Nr4a3', 'Nr5a1', 'Nr5a2', 'Nr6a1', 'Nsd1', 'Nrf1', 'Nfx1', 'Nfya', 'Nfyb', 'Npm1', 'Olig1', 'Olig2', 'Onecut1', 'Onecut2', 'Otx2', 'Ovol2', 'Pax1', 'Pax3', 'Pax6', 'Pax9', 'Prrx1', 'Prrx2', 'Pitx2', 'Per1', 'Per2', 'Per3', 'Ppargc1b', 'Ppara', 'Pparg', 'Ppard', 'Pgs1', 'Plag1', 'Plagl1', 'Plagl2', 'Parp1', 'Parp12', 'Pabpn1', 'Pcbp1', 'Pcbp2', 'Pcbp4', 'Pbrm1', 'Pcgf2', 'Pcgf6', 'Pqbp1', 'Polr2a', 'Polr2b', 'Polr2k', 'Pbx3', 'Pbx4', 'Pgr', 'Pdcd7', 'Phb2', 'Preb', 'Pml', 'Psmc5', 'Pias1', 'Pias2', 'Pias3', 'Pias4', 'Pura', 'Purb', 'Phtf1', 'Rreb1', 'Rbpj', 'Rsl1', 'Rfx1', 'Rfx2', 'Rfx4', 'Rfx5', 'Rfxank', 'Rfxap', 'Rel', 'Rax', 'Rb1', 'Rbbp4', 'Rbbp7', 'Rbl1', 'Rara', 'Rarb', 'Rarg', 'Rxra', 'Rxrb', 'Rpl7', 'Rpl7a', 'Ring1', 'Rnf14', 'Rnf2', 'Rnf25', 'Rnf4', 'Runx2', 'Runx1t1', 'Sall1', 'Sall2', 'Sall3', 'Safb', 'Safb2', 'Scx', 'Scrt1', 'Srf', 'Scmh1', 'Scml2', 'Stat1', 'Stat3', 'Stat4', 'Stat5a', 'Stat5b', 'Stat6', 'Six3', 'Six4', 'Sim2', 'Sirt3', 'Ski', 'Snapc4', 'Satb1', 'Satb2', 'Sra1', 'Srebf2', 'Srebf1', 'Strn', 'Ssrp1', 'Supt16', 'Supt4a', 'Supt5', 'Suv39h1', 'Suv39h2', 'Suz12', 'Tdg', 'Tox', 'Thra', 'Thrap3', 'Thrb', 'Trip11', 'Tef', 'Topors', 'Sp1', 'Sp3', 'Sp4', 'Tcea2', 'Tcf12', 'Tcf20', 'Tcf3', 'Tcf7l1', 'Tcf7l2', 'Tfam', 'Tfcp2', 'Tfdp1', 'Tfdp2', 'Tfe3', 'Ttf1', 'Tada3', 'Sin3a', 'Sin3b', 'Tbl1x', 'Tle1', 'Tle2', 'Tle3', 'Tle4', 'Tle6', 'Trp53', 'Trp63', 'Trrap', 'Mdm2', 'Mdm4', 'Tsn', 'Tgs1', 'Trim24', 'Trim25', 'Trim28', 'Trim30a', 'Trim33', 'Tsc2', 'Ubp1', 'Ubtf', 'Usf1', 'Usf2', 'Uaca', 'Mafb', 'Mafg', 'Mafk', 'Rela', 'Vps72', 'Vezf1', 'Vax1', 'Vhl', 'Zrsr1', 'Zc3h8', 'Zeb1', 'Zeb2', 'Zbtb14', 'Zbtb16', 'Zbtb17', 'Zbtb33', 'Zscan21', 'Zfp106', 'Zfp110', 'Zfp128', 'Zfp148', 'Zfp239', 'Zfp263', 'Zfp326', 'Zfp354a', 'Zfp422', 'Zfp423', 'Zfp467', 'Zfp472', 'Zfp503', 'Zfp60', 'Zfp64', 'Zfp668', 'Zfp68', 'Zfp90', 'Zic1', 'Zic2', 'Zic3', 'Zic4', 'Zic5', 'Zfpm1', 'Zfpm2', 'Zkscan17', 'Zmym2', 'Zhx1', 'Zhx2', 'Adnp', 'Aebp1', 'Aebp2', 'Ank1', 'Ankfy1', 'Atf1', 'Bard1', 'Zfp36l1', 'Camk4', 'Cbfb', 'Cbx2', 'Cbx3', 'Cbx4', 'Ccnk', 'Cdk2', 'Cdk4', 'Chgb', 'Dach1', 'Dnmt1', 'Dnmt3a', 'Dr1', 'Drg1', 'Ebf2', 'Ebf3', 'Ell', 'Ercc2', 'Ercc3', 'Erg', 'Etv6', 'Bcl11a', 'Fah', 'Fem1a', 'Fem1b', 'Foxn2', 'Gabpb1', 'Gas7', 'Mtpn', 'Baz1a', 'Hes1', 'Hey2', 'Foxj1', 'Hipk2', 'Hira', 'Hmgn2', 'Hmg20b', 'Hsf2', 'Elavl2', 'Ifnar2', 'Ikbkb', 'Ikbkg', 'Ilf3', 'Invs', 'Irx1', 'Irx2', 'Klf2', 'Klf3', 'Hivep3', 'Ldb1', 'Ldb2', 'Lhx5', 'Lhx6', 'Lhx8', 'Lmo2', 'Lmo4', 'Maf', 'Mbd3', 'Mcm6', 'Mcm7', 'Mef2c', 'Rab8a', 'Men1', 'Mid1', 'Mllt10', 'Mpl', 'Nab1', 'Naca', 'Ndn', 'Nedd8', 'Nfatc2ip', 'Nfe2l2', 'Nfib', 'Nfkbib', 'Nfkbil1', 'Nfyc', 'Nkx2-2', 'Sqstm1', 'Otp', 'Otx1', 'Pbx2', 'Pdcd11', 'Enpp2', 'Peg3', 'Phf2', 'Papola', 'Cnot7', 'Pou3f3', 'Ppargc1a', 'Ppp3cb', 'Ppp5c', 'Prkar1a', 'Prox1', 'Psmc3', 'Ptma', 'Rab11b', 'Rab18', 'Rai1', 'Rbl2', 'Rfx3', 'Rnps1', 'Rorc', 'Polr2c', 'Polr2j', 'Ruvbl2', 'Rxrg', 'Khdrbs1', 'Sap18', 'Ostf1', 'Siah2', 'Sim1', 'Six5', 'Six6', 'Ighmbp2', 'Snrpb', 'Sox12', 'Zfp143', 'Stag1', 'Stat2', 'Strap', 'Taf1b', 'Tbp', 'Tbx15', 'Tcea1', 'Tcea3', 'Phf1', 'Thrsp', 'Tial1', 'Tmpo', 'Tnfaip3', 'Dedd', 'Ppp1r13b', 'Trip6', 'Tob1', 'Trp73', 'Tsg101', 'Baz1b', 'Zfp1', 'Zfp105', 'Zfp13', 'Zfp2', 'Zfp207', 'Zfp27', 'Zfp35', 'Zfp36', 'Zfp37', 'Zfp39', 'Zfp40', 'Zfp41', 'Zfp46', 'Zfp51', 'Zfp52', 'Zfp54', 'Zfp59', 'Zfp61', 'Zfp9', 'Zfp93', 'Zfp94', 'Zfp97', 'Zfx', 'Zim1', 'Ash2l', 'Bpnt1', 'Dido1', 'Dazap2', 'Ing1', 'Map3k12', 'Mapk1', 'Mapk8', 'Zfp146', 'Cops5', 'Polr3e', 'Sh3d19', 'Zfp275', 'Trp53bp1', 'Zfp354b', 'Ing4', 'Limd1', 'Mga', 'Zfp292', 'Pdlim4', 'Hnf4g', 'Zfp354c', 'Cbx8', 'Sirt6', 'Fbxw7', 'Nrbf2', 'Lsm4', 'Keap1', 'Ppp2r1a', 'Ccnc', 'Ppp1r10', 'Carhsp1', 'Pla2g6', 'Psmd10', 'Htatip2', 'Hif3a', 'Rab11a', 'Ddx20', 'Deaf1', 'Pdlim1', 'Irx5', 'Lrrc6', 'Tcerg1', 'Heyl', 'Zfp386', 'Rbm14', 'Zfp113', 'Trip4', 'Ruvbl1', 'Pmfbp1', 'Zfp235', 'Zfp111', 'Usp49', 'Zfp112', 'Jmy', 'Hic2', 'Shank3', 'Carm1', 'Nrip2', 'Foxb1', 'Sirt2', 'Nmi', 'Asb1', 'Asb4', 'Asb2', 'Asb3', 'Znrd1', 'Polr2e', 'Polr2l', 'Zmynd11', 'Mcm8', 'Ctnnbl1', 'Ccnh', 'Hmg20a', 'Fank1', 'Ssbp2', 'Zdhhc6', 'Yaf2', 'Polr3d', 'Nrarp', 'Rnf141', 'Psmd9', 'Pfdn1', 'Zfp606', 'Dedd2', 'Xab2', 'Rabl3', 'Polr2g', 'Sec14l2', 'Sncaip', 'Nudt12', 'Gtf2e2', 'Zdhhc21', 'Sirt5', 'Phf5a', 'Ankra2', 'Gtf2f2', 'Tulp4', 'Cnot8', 'Ppp1r12b', 'Zfp219', 'Polr2i', '2810021J22Rik', 'Dpf3', 'Basp1', 'Asb12', 'Polr3b', 'Arhgap17', 'Bbx', 'Zcchc8', 'Prdm16', 'Prdm5', 'Zdhhc1', 'Zfp597', 'Fbxo24', 'Fmnl2', 'Pogk', 'Ing3', 'Phf7', 'Ddx54', 'Phf10', 'Asb6', 'Zfp131', 'Ssbp3', 'Bclaf1', 'Zfp248', 'Wasl', 'Nptxr', 'Hbp1', 'Rnf38', 'Btbd11', 'Zdhhc16', 'Gtf2e1', 'Cxxc1', 'Xrcc3', 'Zfp84', 'Hsf2bp', 'Prr3', 'Sirt4', '1700003F12Rik', 'Acyp2', 'Rai14', 'Asb5', 'Snip1', 'Ssbp4', 'Zfp142', 'Lbh', '4930522L14Rik', 'Helz', 'Asb8', 'Brd8', 'Brpf1', 'Sp2', '9130019O22Rik', 'Polr3h', 'Zfp319', 'Abtb1', 'Mynn', 'Zfp202', 'Gtf2a1', 'Sirt1', 'Dach2', 'Maged1', 'Ptges2', 'Zfp451', 'Taf13', 'Zfp282', 'Taf5l', 'Snapc2', 'Ankrd10', 'Rcor1', 'Rab15', 'Bmyc', 'Chd4', 'Zdhhc15', 'Ankhd1', 'Uhrf2', 'Lmo1', 'Zfp91', 'Dek', 'Dbx2', 'Lmx1a', 'Brdt', 'Pawr', 'Baz2a', 'Mta3', 'Ivns1abp', 'Ankrd6', 'Ubn1', 'Zfp287', 'Zfp617', 'Ash1l', 'Clp1', 'Ell2', 'Dot1l', 'Hmgcs1', 'Sirt7', 'Zfp180', '6720489N17Rik', 'Zswim4', 'L3mbtl2', 'Polrmt', 'Gls2', 'Ugp2', 'Btf3', 'Zfp523', 'Trerf1', 'Taf5', 'Zfp281', 'Pms1', 'Zdhhc5', 'Ebf4', 'Nkx2-4', 'Asxl1', 'Ppp1r16b', 'Zfp334', 'Pbxip1', 'Ddx58', 'Zfp189', 'Glis1', 'Tardbp', 'Phf13', 'Creb5', 'Hdac11', 'Ppp1r12c', 'Zfp592', 'Zfp553', 'Zfp612', 'Zfp426', 'Rab8b', 'Scap', 'Tceal1', 'BC024063', 'Homez', 'Fem1c', 'Setbp1', 'St18', 'Carf', 'Dmrta2', 'Polr2h', 'Zfp277', 'Shprh', 'Phf12', 'Zbtb1', 'Caskin1', 'Gtf3c4', 'Gcdh', 'BC024139', 'Zfp398', 'A630089N07Rik', 'Phf8', 'Zhx3', 'Zfp82']
['Arid3c', 'Bag1', 'Cebpa', 'Cited1', 'Cited2', 'Cited4', 'E2f1', 'E2f2', 'E2f7', 'Elf4', 'Elf5', 'Eid2', 'Fosb', 'Fev', 'Gata1', 'Gata2', 'Gata4', 'Gsx1', 'Hmx2', 'Hnf1a', 'Hnf1b', 'Ikzf3', 'Irx6', 'Klf1', 'Lhx3', 'Lmx1b', 'Nkx2-9', 'Nkx6-1', 'Nanog', 'Pou3f1', 'Pou4f1', 'Prdm1', 'Spdef', 'Sertad1', 'Sox15', 'Sox17', 'Sox18', 'Sox3', 'Sp7', 'Tal1', 'Tal2', 'Tbx1', 'Tbx21', 'Tbx5', 'Tbx6', 'Tbr1', 'Taf10', 'Taf6l', 'Taf9', 'Tead2', 'Tead4', 'Tgif1', 'Txk', 'Snrpb2', 'Ascl1', 'Ascl3', 'Atf3', 'Apex1', 'Arx', 'Atoh7', 'Aire', 'Bmp4', 'T', 'Creb3l3', 'Creb3l4', 'Cdc6', 'Crxos', 'Dlx2', 'Dlx5', 'Dlx6', 'Egr1', 'Egr3', 'Emx1', 'En2', 'Esx1', 'Foxa2', 'Foxc1', 'Foxd2', 'Foxf1', 'Foxl2', 'Foxm1', 'Foxp3', 'Fhl2', 'Gtf2a1l', 'Gcm1', 'Grhl3', 'Gadd45a', 'Hhex', 'Hnf4a', 'Hoxa1', 'Hesx1', 'Hic1', 'Ifi204', 'Irf2bp1', 'Irf7', 'Irf8', 'Jun', 'Meox1', 'Meox2', 'Mesp2', 'Msx1', 'Msx2', 'Msc', 'Myb', 'Myc', 'Mef2b', 'Myog', 'Nhlh2', 'Neurod1', 'Neurod6', 'Npas4', 'Sp100', 'Nfkbie', 'Nfe2', 'Nr0b1', 'Nr1h5', 'Onecut3', 'Ovol1', 'Pax2', 'Pax5', 'Pax7', 'Pax8', 'Prrxl1', 'Pitx1', 'Pitx3', 'Pdx1', 'Pin1', 'Pcbd1', 'Rbpjl', 'Runx1', 'Runx3', 'Sall4', 'Snai2', 'Snai3', 'Spi1', 'Tcf15', 'Tcf7', 'Tfap2a', 'Tfap2e', 'Tfap4', 'Tfec', 'Tgfb1i1', 'Trib3', 'Twist2', 'Mafa', 'Maff', 'Mycn', 'Vav1', 'Vgll2', 'Vsx2', 'Vdr', 'Zbtb7b', 'Zfy1', 'Zik1', 'Zmynd10', 'Ahrr', 'Aip', 'Alx4', 'Neurog2', 'Bcl6b', 'Bnc1', 'Brca1', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d', 'Dlx1', 'Foxc2', 'Hcls1', 'Foxn1', 'Hmgb3', 'Hmga2', 'Hmx3', 'Foxa3', 'Htr5a', 'Irf4', 'Lhx9', 'Lyl1', 'Ascl2', 'Mcm3', 'Mcm2', 'Mcm4', 'Mcm5', 'Notch4', 'Uhrf1', 'Musk', 'Pou2f3', 'Scand1', 'Ppp1r1b', 'Shox2', 'Zfp57', 'Zfp92', 'Vax2', 'Abt1', 'Dmrt1', 'Plrg1', 'Insm1', 'Irf6', 'Zfp108', 'Nupr1', 'Papolb', 'Rps6ka4', 'Ankrd2', 'Insm2', 'Zfp109', 'Fhl5', 'Sap30', 'Dmap1', '4930430A15Rik', 'Asb11', 'Zfp580', 'Trip13', '4931423N10Rik', '4933429H19Rik', 'Lsm11', 'Nkd2', 'Zbtb3', 'Hspb9', 'Tbx18', 'Asb15', 'Sp6', 'Tcf19', 'Ankrd1', 'Tbx10', 'Rem2', 'Sox30', 'Myocd', 'Tgif2', 'Gbx1', 'Zfp454', 'Bsx', 'Obox3']

for gene in tfcols:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        vmax="p90.0",
        na_color="#9A9FB080",
        save="-tf1-" + gene + save_PRJNA779749,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf1-Ddx5-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf1-Rora-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf1-Rorb-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf1-Sox6-best_xgboost-subregional_supervised-PRJNA779749-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

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for gene in tfcols2:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        na_color="#9A9FB080",
        save="-tf2" + gene + save_PRJNA779749,
)

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for gene in tfcols3:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
        vmin=0.0,
        na_color="#9A9FB080",
        save="-tf3" + gene + save_PRJNA779749,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA779749,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA779749,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA779749,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=tfcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-tf" + save_PRJNA779749,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA779749,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA779749,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA779749,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=tfcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-tf" + save_PRJNA779749,
)

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Romanov et al. 2020

PRJNA548917_counts = sc.read_h5ad(
    f"data/PRJNA548917/data/PRJNA548917-astrocytes_dataset-{cb_fpr}-scenic_plus.h5ad"
)

PRJNA548917_regulons = sc.read_h5ad(
    f"data/PRJNA548917/data/PRJNA548917-astrocytes_dataset-{cb_fpr}-regulons.h5ad"
)

adata_eval2 = PRJNA548917_counts.copy()
# sc.pp.filter_genes(adata_eval2, min_cells=5) # filter genes should be matched with training set
sc.pp.normalize_total(adata_eval2, target_sum=1e4)
sc.pp.log1p(adata_eval2)
sc.pp.regress_out(adata_eval2, ["nCount_RNA", "percent_mito_ribo"])

normalizing counts per cell

    finished (0:00:00)

regressing out ['nCount_RNA', 'percent_mito_ribo']

    sparse input is densified and may lead to high memory use

    finished (0:00:37)

df_eval2 = pd.DataFrame(
    adata_eval2.X, columns=adata_eval2.var_names, index=adata_eval2.obs_names
)

df_eval2 = df_eval2.loc[
    :, [g for g in uni_fs if g in adata_eval2.var_names]
]
df_eval2.head()

	0610005C13Rik	Lpl	Lpp	Lrat	Lrba	Lrch3	0610043K17Rik	Lrguk	Lrig1	1110008P14Rik	...	Lncbate6	Lncpint	Lnpep	Lonrf2	Lor	Loxl2	Lpar1	Lpar3	Lpgat1	Lpin2
SRR9303102_AAACCTGAGCGAGAAA-1	-0.000767	-0.009159	1.278495	-0.000441	-0.448519	-0.341000	-0.006555	-0.011507	1.076447	-0.023362	...	-0.000029	-0.142432	-0.305344	-0.108615	-0.006866	-0.000033	-0.021097	-0.000253	-0.020914	-0.165400
SRR9303102_AAACGGGCATACGCTA-1	-0.000399	-0.012045	1.322034	-0.000351	-0.427408	-0.328283	-0.006129	-0.010047	-1.239804	-0.029337	...	-0.000012	-0.130144	-0.299177	-0.106252	-0.007449	0.000023	-0.024482	-0.000053	-0.021815	-0.160199
SRR9303102_AAAGCAATCATAGCAC-1	-0.000379	-0.018577	-1.071225	-0.000224	-0.447083	-0.336911	-0.004719	-0.011476	-1.267177	-0.045693	...	-0.000286	-0.104236	-0.322090	-0.112885	-0.007847	-0.000148	-0.032368	-0.000404	-0.023297	-0.175200
SRR9303102_AAAGTAGTCAAACAAG-1	0.000579	-0.024079	1.258349	-0.000028	-0.385526	-0.300849	-0.004047	-0.007198	-1.214069	-0.056186	...	-0.000154	-0.080211	-0.298683	-0.104593	-0.009249	0.000052	-0.038748	0.000229	-0.025191	-0.156823
SRR9303102_AAAGTAGTCTCCCTGA-1	-0.002199	0.009555	2.111337	-0.000934	2.063579	-0.379524	-0.009842	-0.015414	-1.289503	0.018707	...	0.000226	-0.219876	-0.302028	-0.109868	-0.004168	-0.000045	0.001115	-0.000603	-0.015726	-0.167684

5 rows × 4126 columns

X_uni_to_PRJNA548917 = X_uni.loc[:, [g for g in uni_fs if g in adata_eval2.var_names]]
subregional_model.fit(X_uni_to_PRJNA548917, codes)

joblib.dump(
    subregional_model,
    f"models/svm/best-logit-subregional_PRJNA548917_astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle",
)

print(X_uni_to_PRJNA548917.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist())

['0610005C13Rik', '0610043K17Rik', '1190005I06Rik', 'Lrp8os2', '1500004A13Rik', 'Lrrc18', '1500015L24Rik', '1600010M07Rik', '1600012H06Rik', 'Lrrc45', 'Lrrc57', '1700003D09Rik', '1700003G18Rik', '1700003M07Rik', '1700008C04Rik', 'Lrrd1', 'Lrrk1', 'Lsm8', '1700022H01Rik', '1700024B05Rik', 'Ltbp1', '1700025F22Rik', '1700027I24Rik', '1700028E10Rik', '1700029I15Rik', '1700030F04Rik', 'Ly86', '1700030N03Rik', '1700034I23Rik', 'Lysmd4', 'M1ap', '1700054A03Rik', '1700060O08Rik', '1700061G19Rik', '1700064H15Rik', 'Mageb18', '1700081N11Rik', '1700084C06Rik', '1700095A13Rik', '1700095J03Rik', 'Mamdc2', 'Mamdc4', 'Maml3', '1700102P08Rik', '1700108F19Rik', 'Mannr', 'Maob', 'Map1b', 'Map2k4', 'Map3k11', '1810020O05Rik', '1810030O07Rik', 'Map4k3', 'Map7', '2010007H06Rik', '2210016F16Rik', '2310030A07Rik', '2310030G06Rik', '2310039L15Rik', 'Masp1', '2310068J16Rik', '2510009E07Rik', '2610001J05Rik', '2600006K01Rik', 'Mboat4', '2610307P16Rik', 'Mccc1os', '2610318N02Rik', '2700033N17Rik', '2700097O09Rik', 'Mcm6', '2810030D12Rik', '2810410L24Rik', 'Mcur1', 'Mdfi', '2810459M11Rik', '2900052N01Rik', 'Mdp1', '3110021N24Rik', '3110039M20Rik', '3110056K07Rik', '3200001D21Rik', '3222401L13Rik', 'Mef2c', 'Meg3', '4731419I09Rik', 'Meikin', 'Meis2', '4732496C06Rik', '4833422C13Rik', 'Metap1d', '4833423E24Rik', 'Mettl11b', 'Mettl17', '4921507P07Rik', '4921511C10Rik', 'Mettl24', '4921539H07Rik', '4930404I05Rik', '4930404N11Rik', 'Mfng', 'Mfsd14b', 'Mfsd2a', 'Mfsd3', '4930415C11Rik', '4930415O20Rik', 'Mfsd7a', 'Mgam', 'Mgam2-ps', '4930432B10Rik', '4930439D14Rik', '4930445B16Rik', '4930447C04Rik', 'Mid1', 'Miip', 'Mill2', '4930455B14Rik', '4930467E23Rik', '4930470G03Rik', 'Mir124-2hg', '4930473A02Rik', 'Mlh3', '4930512B01Rik', 'Mmp11', 'Mmrn2', '4930529K09Rik', 'Mogat1', '4930545L23Rik', 'Mok', '4930549C15Rik', 'Morn2', '4930554G24Rik', '4930554H23Rik', 'Morn5', '4930556G01Rik', '4930556J24Rik', '4930558K02Rik', 'Mpg', 'Mphosph9', 'Mpped1', 'Mpped2', '4930579C12Rik', '4930589L23Rik', 'Mrgpra6', '4930594M22Rik.1', '4931403E22Rik', 'Mroh7', '4931413K12Rik', '4932411K12Rik', '4932435O22Rik', '4932443L11Rik', 'Mrpl42', '4933403O08Rik', '4933406F09Rik', 'Mrps18b', '4933408J17Rik', '4933421A08Rik', 'Msantd2', 'Msh6', '4933431E20Rik', 'Msl3', '4933431K23Rik', 'Msx2', 'Mta1', 'Mta3', '5031434O11Rik', 'Mtcp1', '5033426O07Rik', 'Mtfp1', 'Mtg1', '5430416N02Rik', 'Mthfs', '5430431A17Rik', 'Mtmr4', '5730522E02Rik', '5830444B04Rik', 'Mttp', 'Mtus1', '5930435M05Rik', '6030498E09Rik', '6330403K07Rik', 'Mus81', 'Mvd', '6430503K07Rik', '6330576A10Rik', '6430553K19Rik', '6430628N08Rik', '6530402F18Rik', '6530409C15Rik', '6530411M01Rik', '6720427I07Rik', 'Mycbp', 'Mycl', '8030453O22Rik', 'Myh6', 'Mylip', '9130008F23Rik', '9130017K11Rik', '9130221H12Rik', 'Myo1f', 'Myoc', 'Myorg', '9330158H04Rik', 'Myrfl', 'Myrip', 'Naa10', '9630001P10Rik', '9630014M24Rik', 'Naip1', '9630028H03Rik', 'Nalcn', 'Napb', 'A230020J21Rik', 'A230051N06Rik', 'Nat8f7', 'Naxe', 'A230108P19Rik', 'A330076C08Rik', 'A330087D11Rik', 'A330094K24Rik', 'A4galt', 'Ncoa6', 'Ncor2', 'A530053G22Rik', 'Ndnf', 'A630001O12Rik', 'A630023A22Rik', 'A730020M07Rik', 'A730049H05Rik', 'Ndufaf4', 'A730063M14Rik', 'A830035A12Rik', 'Necab3', 'Necap1', 'Nectin1', 'A930006K02Rik', 'Nefh', 'Nek11', 'Nek5', 'Nek8', 'AA465934', 'AB124611', 'Nell2', 'AC163638.2', 'AI429214', 'AI463229', 'Nfic', 'AI849053', 'Nfkbia', 'Nfkbib', 'Nfkbil1', 'AU023762', 'AW146154', 'Nhs', 'AY036118', 'Nifk', 'Aarsd1', 'Aasdhppt', 'Aass', 'Aatk', 'Nkain2', 'Nkd1', 'Nkx2-1', 'Nkx2-2', 'Nkx2-2os', 'Nkx2-4', 'Abcb1a', 'Abcb9', 'Nlgn2', 'Abcc10', 'Abcc2', 'Abcf2', 'Nme3', 'Abhd15', 'Nmnat2', 'Nmur1', 'Abl1', 'Ablim2', 'Abt1', 'Acap3', 'Notum', 'Acat2', 'Acat3', 'Ackr3', 'Ackr4', 'Acp1', 'Acsbg2', 'Nr1d1', 'Nr1d2', 'Nr2f2', 'Actr3b', 'Acvr1c', 'Acy3', 'Adam11', 'Nrg2', 'Adam5', 'Adamts12', 'Nsd2', 'Nsdhl', 'Nsf', 'Adamts2', 'Nsmce1', 'Nsmce3', 'Adat2', 'Ntn1', 'Ntn3', 'Ntng1', 'Nuak2', 'Adgrb2', 'Adgrf4', 'Adgrg2', 'Adgrl2', 'Nudt16l1', 'Adra1b', 'Adrb1', 'Nupr1l', 'Aff3', 'Afm', 'Agap2', 'Agbl1', 'Oacyl', 'Ahctf1', 'Ahdc1', 'Ahsa1', 'Ogdhl', 'Ogfod2', 'Ogfod3', 'Ajm1', 'Olfml2b', 'Olfr1511', 'Akr1b3', 'Olfr455', 'Akt2', 'Olfr655', 'Olfr66', 'Olig2', 'Omg', 'Onecut2', 'Opa1', 'Alkal2', 'Alkbh7', 'Alpl', 'Alyref2', 'Amdhd2', 'Amer2', 'Ammecr1', 'Amot', 'Otoa', 'Amy1', 'Angel1', 'Otx1', 'Otx2', 'Otx2os1', 'Ovca2', 'Angptl4', 'Ankfn1', 'P2rx3', 'P2ry12', 'P2ry13', 'P2ry14', 'P2ry6', 'Ankrd22', 'P3h4', 'Pacsin1', 'Pacsin2', 'Padi2', 'Paf1', 'Anks1b', 'Ankzf1', 'Anln', 'Pank1', 'Paox', 'Anxa9', 'Aox1', 'Pard3bos1', 'Ap5s1', 'Apc2', 'Pawr', 'Pax5', 'Apln', 'Apmap', 'Apobec4', 'Aqp5', 'Pcdhb2', 'Aqp9', 'Ar', 'Pcdhb22', 'Pcgf1', 'Pck2', 'Arg2', 'Arhgap23', 'Pcsk2', 'Pcsk2os1', 'Arhgap31', 'Arhgap33', 'Arhgap40', 'Arhgap6', 'Pdcd1lg2', 'Arhgef10', 'Pde10a', 'Arhgef18', 'Arhgef28', 'Arhgef9', 'Arid3a', 'Pdgfb', 'Arl13a', 'Arl4d', 'Pdlim1', 'Armc2', 'Armcx4', 'Pdzd7', 'Arntl', 'Pdzph1', 'Arpp21', 'Peak1os', 'Pebp4', 'Peg3', 'Arsj', 'Per2', 'Arx', 'Arvcf', 'Asb2', 'Pfkfb4', 'Ascl4', 'Asic1', 'Asic4', 'Asna1', 'Pglyrp1', 'Phactr2', 'Phf21b', 'Atic', 'Phldb2', 'Atoh7', 'Pi15', 'Atp23', 'Atp6v1e2', 'Atxn2l', 'Pitpnm3', 'Pitpnm2os2', 'Atxn7l2', 'Pitx2', 'Atxn7l3', 'Piwil4', 'Pkd1l2', 'Pkdcc', 'Pkhd1', 'Pkib', 'B130024G19Rik', 'Pla2g10', 'Pla2r1', 'Plat', 'Platr27', 'Platr8', 'Plbd1', 'B430010I23Rik', 'Plcb3', 'Pld5', 'B830042I05Rik', 'Plek2', 'Plekha6', 'BC017158', 'BC034090', 'Plekho2', 'BC037032', 'Plin3', 'Plk2', 'BC050972', 'BC051226', 'Plppr5', 'Bach2os', 'Bag3', 'Bahcc1', 'Baiap2', 'Baiap3', 'Bambi', 'Plxnc1', 'Pmepa1', 'Barx2', 'Pmepa1os', 'Bbox1', 'Bbs1', 'Bcam', 'Bcar3', 'Pnpla2', 'Pnrc1', 'Bcas3os1', 'Podnl1', 'Pogk', 'Poglut1', 'Poglut2', 'Pold2', 'Bcl2l2', 'Polr1c', 'Becn1', 'Begain', 'Bet1l', 'Bicd2', 'Pou3f4', 'Bloc1s2', 'Bmp5', 'Bmp7', 'Bmx', 'Bnip2', 'Boc', 'Ppip5k2', 'Ppp1r12c', 'Ppp1r13b', 'Brd1', 'Ppp1r15a', 'Ppp1r16b', 'Brd3os', 'Ppp1r1b', 'Ppp1r26', 'Brip1os', 'Ppp1r3a', 'Ppp1r3d', 'Ppp1r9b', 'Ppp2r2c', 'Bsx', 'Btbd11', 'Btbd3', 'Btg2', 'Bves', 'Prag1', 'Prcd', 'C030007H22Rik', 'Prdm1', 'C030018K13Rik', 'C030032O16Rik', 'C030034L19Rik', 'Prdm8', 'Prdx4', 'Prelp', 'C1galt1c1', 'Prickle1', 'Prkaa2', 'C1s1', 'C2', 'Prkcb', 'Prkcd', 'C230034O21Rik', 'Prkcz', 'C2cd4c', 'C2cd4d', 'C330002G04Rik', 'C330013E15Rik', 'C4b', 'C530025M09Rik', 'C5ar1', 'C5ar2', 'C78334', 'Prox2', 'C8g', 'Prpf31', 'Cab39l', 'Prr11', 'Prrc2b', 'Cacna1h', 'Cacna2d2', 'Prrt1', 'Prrt2', 'Prss27', 'Prss36', 'Prss41', 'Prss45', 'Caln1', 'Calml4', 'Camk2a', 'Camk2b', 'Psme2b', 'Capsl', 'Ptch2', 'Ptchd4', 'Car15', 'Car3', 'Car8', 'Ptgs1', 'Pth1r', 'Carmil2', 'Casp6', 'Ptpn7', 'Catsperb', 'Ptprh', 'Catspere1', 'Ptpro', 'Cbfa2t3', 'Purg', 'Ccbe1', 'Pygm', 'Ccdc134', 'Ccdc146', 'Ccdc152', 'Ccdc162', 'Rab11fip4', 'Ccdc171', 'Rab20', 'Rab26os', 'Ccdc22', 'Ccdc24', 'Rab33b', 'Ccdc34os', 'Ccdc39', 'Ccdc38', 'Ccdc84', 'Ccdc85c', 'Ccdc86', 'Rad21l', 'Ccn1', 'Ralgapb', 'Ccn6', 'Ralyl', 'Rap1gap', 'Rapgef5', 'Cd164l2', 'Rasgrf1', 'Rasl11a', 'Rasl11b', 'Rassf2', 'Raver2', 'Rax', 'Rbis', 'Cd82', 'Rbm42', 'Cd96', 'Rbms2', 'Rbms3', 'Rbmxl1', 'Cdc42ep1', 'Rbpms', 'Cdc42ep4', 'Rbsn', 'Rcor2', 'Rcsd1', 'Cdh23', 'Recql5os1', 'Cdk10', 'Rev1', 'Cdr1os', 'Ceacam1', 'Rfxap', 'Cebpb', 'Rgl2', 'Rgs2', 'Cenpt', 'Rhcg', 'Rhd', 'Rhobtb1', 'Rhobtb2', 'Ribc2', 'Rif1', 'Rimbp2', 'Ces5a', 'Ripor2', 'Cfap100', 'Cfap161', 'Rmi1', 'Rmnd5a', 'Cfap46', 'Cfap52', 'Rnd1', 'Cfap69', 'Rnf121', 'Cfap77', 'Rnf122', 'Cfb', 'Cfp', 'Rnf152', 'Chd5', 'Rnf24', 'Rnf5', 'Chil1', 'Robo2', 'Rogdi', 'Ror1', 'Ror2', 'Chrac1', 'Chrdl1', 'Rpe65', 'Chst3', 'Ciart', 'Cib1', 'Cic', 'Rrp15', 'Rsf1os1', 'Rsf1os2', 'Rslcan18', 'Cklf', 'Ckmt2', 'Clcn6', 'Rubie', 'Rufy2', 'Rundc3a', 'Clec3b', 'Clic3', 'Ryr1', 'Clic6', 'S100pbp', 'S1pr5', 'Sall3', 'Samd12', 'Sap30bpos', 'Sap30l', 'Cmtr2', 'Pigm', 'Cndp2', 'Cnnm3', 'Scg5', 'Cntn2', 'Cntnap1', 'Scly', 'Scpep1os', 'Scrib', 'Col1a1', 'Col1a2', 'Scrt1', 'Scube1', 'Scrt2', 'Col25a1', 'Col4a3', 'Sdk2', 'Sds', 'Col9a2', 'Colec12', 'Sec14l5', 'Selenbp1', 'Coq10b', 'Coq6', 'Corin', 'Coro2b', 'Sema3c', 'Sema3b', 'Sema3d', 'Sema4a', 'Sema3f', 'Cox6a2', 'Sema5a', 'Sema5b', 'Sema7a', 'Sept4', 'Cpne6', 'Serpinb5', 'Serpinb9', 'Serpine1', 'Serpine3', 'Cr2', 'Crebzf', 'Creld2', 'Sez6l', 'Sf3b4', 'Sfmbt1', 'Crlf2', 'Sfrp1', 'Crocc', 'Sft2d2', 'Sfxn2', 'Sgcz', 'Crybg2', 'Crym', 'Sgsm1', 'Sh2b1', 'Csmd2', 'Csrnp3', 'Sh3gl2', 'Sh3glb2', 'Cstdc2', 'Sh3rf2', 'Sh3rf3', 'Sh3tc2', 'Shank2', 'Ctif', 'Shh', 'Shisa3', 'Shisa6', 'Shmt1', 'Ctsh', 'Shmt2', 'Shprh', 'Shroom2', 'Sik1', 'Cutal', 'Sim2', 'Sipa1l1', 'Sipa1l3', 'Cxcr4', 'Sirt7', 'Six3', 'Six3os1', 'Cyb561', 'Cyb561d1', 'Skida1', 'Cyp27a1', 'Cyp2c55', 'Slc13a3', 'Cyp2c69', 'Slc13a5', 'Slc14a2', 'Slc15a1', 'Slc16a2', 'Cyp4f15', 'Slc17a9', 'Cyp51', 'Cyth1', 'Slc22a12', 'D030046N08Rik', 'Slc22a20', 'D030068K23Rik', 'Slc22a27', 'D130079A08Rik', 'Slc24a2', 'Slc24a4', 'Slc25a19', 'D630003M21Rik', 'Slc25a29', 'Slc25a47', 'Slc26a1', 'Slc26a2', 'Dach1', 'Dancr', 'Slc2a5', 'Dapk1', 'Dapk2', 'Slc30a4', 'Dbil5', 'Dcc', 'Dcdc2a', 'Dcdc2b', 'Slc38a1', 'Slc39a1', 'Ddias', 'Slc45a3', 'Slc46a3', 'Ddx3y', 'Slc5a11', 'Slc5a7', 'Defb39', 'Slc6a7', 'Slc7a2', 'Slc7a3', 'Deptor', 'Derl3', 'Dgat2', 'Slc9b1', 'Dgki', 'Dhcr24', 'Dhcr7', 'Slf1', 'Dhrs3', 'Slitrk2', 'Dhx58os', 'Slx1b', 'Diaph3', 'Dicer1', 'Smad3', 'Dio2', 'Diras2', 'Disc1', 'Dixdc1', 'Dkk3', 'Dlg4', 'Smg8', 'Dlk1', 'Smim22', 'Dlx2', 'Dlx4', 'Dmp1', 'Dmrta2', 'Smyd1', 'Dnaic1', 'Dnaja4', 'Snhg3', 'Snhg4', 'Snhg9', 'Dnajc27', 'Dnajc30', 'Dnd1', 'Sntg1', 'Doc2a', 'Snx20', 'Dock10', 'Dock11', 'Snx25', 'Dock5', 'Snx30', 'Dok6', 'Dok7', 'Dolpp1', 'Sod3', 'Sorcs3', 'Sowaha', 'Sox13', 'Sox4', 'Drc1', 'Sp5', 'Dusp1', 'Dusp27', 'Dusp6', 'Spatc1l', 'Specc1l', 'Spef2', 'Dynlt1b', 'Spg20', 'Dzank1', 'Spin4', 'Spink2', 'E030042O20Rik', 'E230032D23Rik', 'E230034D01Rik', 'Spsb4', 'Sqle', 'Ece1', 'Echdc2', 'Srcap', 'Srcin1', 'Srd5a3', 'Edrf1', 'Eef1akmt3', 'Eef1akmt4', 'Ssbp3', 'Ssbp4', 'Efcab10', 'Ssc4d', 'Sspo', 'Efcc1', 'Efhd1', 'Efhd2', 'Efna2', 'St3gal2', 'Eftud2', 'St6gal2', 'Egfem1', 'Egfl6', 'St6galnac3', 'St6galnac5', 'Egln3', 'Egr1', 'St8sia3os', 'Ehd2', 'Ehd3', 'Stab2', 'Stambpl1', 'Stat4', 'Stimate', 'Elavl4', 'Elfn2', 'Stk32a', 'Elmo1', 'Elmo2', 'Elmod1', 'Stk39', 'Elovl2', 'Stom', 'Elovl6', 'Emc4', 'Eml2', 'Stx1b', 'Emsy', 'Emx2', 'Emx2os', 'Enkd1', 'Enkur', 'Sulf2', 'Enpp2', 'Sult1a1', 'Sult2a6', 'Enthd1', 'Entpd3', 'Susd4', 'Epha3', 'Epha5', 'Svil', 'Ephb3', 'Swap70', 'Syn3', 'Eps8l1', 'Syne4', 'Syngap1', 'Synj1', 'Ercc3', 'Ercc6l2', 'Syt3', 'Sytl5', 'Esrrb', 'Ess2', 'Tada1', 'Taf13', 'Etv4', 'Etv5', 'Taf7l', 'Eva1b', 'Tafa1', 'Exd1', 'Exoc2', 'Extl1', 'Eya1', 'Eya4', 'F2', 'Tbc1d10c', 'F5', 'F630206G17Rik', 'Fads3', 'Fam126b', 'Fam163a', 'Fam163b', 'Fam177a', 'Fam181a', 'Tdrd3', 'Fam193b', 'Tek', 'Fam228a', 'Fam229a', 'Fam3c', 'Fam43b', 'Fam57b', 'Tex22', 'Fam92b', 'Tex9', 'Fap', 'Far1os', 'Fas', 'Tgfbr3', 'Tgfbr3l', 'Tgif1', 'Fbll1', 'Fbln1', 'Fbln5', 'Fbp1', 'Fbxl14', 'Fbxo15', 'Thoc3', 'Thrsp', 'Ticam2', 'Fbxo9', 'Fcgr2b', 'Fcgr3', 'Fcrls', 'Fdft1', 'Fem1a', 'Tle2', 'Tlnrd1', 'Fgf10', 'Fgf13', 'Fgf20', 'Fgf5', 'Fgf9', 'Fgfr4', 'Tmcc2', 'Fhad1', 'Fhl1', 'Tmeff1', 'Fibp', 'Tmem100', 'Filip1l', 'Firre', 'Tmem123', 'Tmem125', 'Tmem132b', 'Tmem140', 'Flrt1', 'Flrt2', 'Flt3l', 'Flvcr1', 'Fmo1', 'Fn3krp', 'Tmem173', 'Fnbp4', 'Fndc5', 'Tmem181a', 'Tmem191c', 'Folr1', 'Folr2', 'Fos', 'Foxa1', 'Tmem204', 'Foxd1', 'Foxd2os', 'Foxg1', 'Tmem203', 'Tmem216', 'Tmem245', 'Frem1', 'Frmpd1os', 'Frmpd4', 'Tmem269', 'Frzb', 'Tmem37', 'Tmem44', 'Tmem50a', 'Fuca1', 'Fuca2', 'Tmem63a', 'Fut4', 'Tmem86a', 'Fzd10', 'Fzd10os', 'Fzd2', 'Fzd4', 'G0s2', 'G630016G05Rik', 'Tmprss6', 'G630093K05Rik', 'Gabra2', 'Tnfrsf19', 'Gabrr2', 'Tnfrsf26', 'Tnfsf11', 'Tnfsf12', 'Tnfsf4', 'Tnfsf8', 'Galnt10', 'Tnk2', 'Galnt2', 'Tnni3', 'Tns1', 'Gas1', 'Togaram2', 'Gas2l2', 'Tomm6os', 'Gata4', 'Gatc', 'Gatd3a', 'Tor1b', 'Tpd52l1', 'Gc', 'Gcnt1', 'Gcnt4', 'Gcnt7', 'Gdf10', 'Trac', 'Gdpd3', 'Gfm1', 'Traj27', 'Ggnbp1', 'Trcg1', 'Trdn', 'Gigyf1', 'Triap1', 'Gins4', 'Gjb2', 'Gjc2', 'Gjc3', 'Gldnos', 'Gle1', 'Gli1', 'Glipr1l3', 'Glipr2', 'Glmp', 'Trim8', 'Glrb', 'Trip6', 'Glt8d2', 'Trmu', 'Trp63', 'Trpm1', 'Gnmt', 'Gnpda2', 'Gnpnat1', 'Trpv2', 'Got2', 'Tshr', 'Tsix', 'Gpatch4', 'Gpc3', 'Tspan4', 'Tspoap1', 'Gpr141', 'Gpr146', 'Gpr153', 'Gpr155', 'Ttc12', 'Gpr162', 'Gpr37', 'Ttc34', 'Gprasp2', 'Gprc5c', 'Ttll5', 'Gpt2', 'Gprin2', 'Gpx8', 'Gramd4', 'Grid2ip', 'Grifin', 'Grik3', 'Grik4', 'Grik5', 'Grin3a', 'Grin3b', 'Grm4', 'Gsdmd', 'Uap1', 'Gssos1', 'Ubash3b', 'Gstt2', 'Gstz1', 'Ube4bos1', 'Ubfd1', 'Ubqln4', 'Ubtd1', 'Ubxn11', 'Gyg', 'Uckl1os', 'Ucma', 'H1f0', 'Uggt2', 'H2-K1', 'Umad1', 'H2-T23', 'Unc13c', 'Unc80', 'Hal', 'Upf1', 'Hap1', 'Hapln1', 'Hccs', 'Ush2a', 'Hdac4', 'Hdc', 'Hdhd5', 'Heg1', 'Usp6nl', 'Hemk1', 'Uty', 'Uvssa', 'Herpud1', 'Hesx1', 'Hexa', 'Heyl', 'Vash1', 'Vasn', 'Vasp', 'Hgh1', 'Higd1b', 'Higd1c', 'Vip', 'Vmn2r15', 'Vmn2r29', 'Hist2h2aa1', 'Hmcn1', 'Hmga1b', 'Hmgb2', 'Hmgcr', 'Hmgcs2', 'Vsig4', 'Hmgn5', 'Vstm4', 'Hnf4aos', 'Wars2', 'Hopx', 'Hoxa1', 'Hpcal1', 'Hpse', 'Hpse2', 'Hr', 'Hrh1', 'Wdr45', 'Hsd11b1', 'Wdr72', 'Wdr76', 'Wdr81', 'Wdr86', 'Hsf3', 'Hspa12a', 'Whamm', 'Wipf1', 'Wls', 'Wnt6', 'Wnt7a', 'Htr1f', 'Hyou1', 'Icosl', 'Id1', 'Ide', 'Idi1', 'Ier2', 'Yif1a', 'Ifi27', 'Ifi47', 'Yy2', 'Zap70', 'Igdcc3', 'Zbtb3', 'Igf2bp3', 'Igsf21', 'Zc3hav1', 'Il10rb', 'Zcwpw2', 'Il15', 'Il1rapl2', 'Zfand2a', 'Il31ra', 'Il33', 'Zfp110', 'Il6ra', 'Zfp157', 'Zfp160', 'Impdh2', 'Zfp217', 'Zfp263', 'Inhbc', 'Inka1', 'Ino80', 'Zfp319', 'Zfp330', 'Zfp335', 'Insig1', 'Zfp335os', 'Insyn2a', 'Zfp385a', 'Zfp414', 'Iqcc', 'Iqch', 'Zfp451', 'Iqub', 'Irak4', 'Zfp513', 'Zfp516', 'Irf7', 'Zfp524', 'Irs2', 'Zfp536', 'Irx3', 'Irx5', 'Zfp592', 'Zfp629', 'Itgbl1', 'Zfp708', 'Itpka', 'Itpr1', 'Izumo4', 'Jag1', 'Zfp799', 'Zfp808', 'Zfp811', 'Jph2', 'Zfp85', 'Jpx', 'Zfp853', 'Jun', 'Junb', 'Kat2a', 'Zfp936', 'Kat8', 'Katnal2', 'Kbtbd12', 'Kcna2', 'Kcnc4', 'Zfp981', 'Kcng1', 'Kcng2', 'Kcng3', 'Kcng4', 'Zfp991', 'Kcnh3', 'Kcnip1', 'Kcnj3', 'Zic1', 'Zic2', 'Zic4', 'Zic3', 'Zim3', 'Kcnk5', 'Kcnq1', 'Kcns3', 'Kcnt1', 'Kctd12b', 'Kctd17', 'Kdm5d', 'Kdm8', 'Khdrbs3', 'Kif13b', 'Kif19a', 'Kif3c', 'Kifc5b', 'Kirrel2', 'Kiss1', 'Klc1', 'Klc2', 'Klhdc7a', 'Klhl10', 'Klhl25', 'Krt8', 'Ky', 'L3hypdh', 'L3mbtl4', 'Lamb3', 'Lao1', 'Lars2', 'Lca5l', 'Lck', 'Lctl', 'Ldhc', 'Ldlr', 'Lenep', 'Leprot', 'Lgals12', 'Lgr6', 'Lgsn', 'Lhfpl2', 'Lhx2', 'Lipo3', 'Lmo3', 'Lmod1', 'Lonrf2', 'Loxl2', 'Lpar1', 'Lpgat1', 'Lpin2']

subregional_model2.fit(X_uni_to_PRJNA548917, codes)

joblib.dump(
    subregional_model2,
    f"models/svm/best-xgboost-subregional_PRJNA548917_astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle",
)

print(X_uni_to_PRJNA548917.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist())

['Lrp1b', 'Lrrc8a', 'Ltbp1', 'Ly6h', 'Macrod1', 'Malat1', 'Map3k5', 'Mbp', 'Mdga2', '3300002A11Rik', 'Medag', '4833423E24Rik', 'Mfge8', 'Mfsd2a', 'Mir9-3hg', '4930549C15Rik', 'Mrps6', 'Msi2', 'Msmo1', 'Mt1', 'Mt2', 'Mt3', 'Myoc', 'Myorg', 'Nbl1', 'Ndufa3', 'Nebl', 'Nefl', 'AC149090.1', 'Nhs', 'AY036118', 'Nkain2', 'Nnat', 'Acot4', 'Actn1', 'Nr2f2', 'Nrarp', 'Nrp1', 'Nrsn1', 'Nrxn1', 'Nrxn3', 'Ntm', 'Adcy8', 'Aff3', 'Agt', 'Ank3', 'Ankfn1', 'Anln', 'Aplp1', 'Apoe', 'App', 'Aqp4', 'Arf3', 'Pcsk1n', 'Pde10a', 'Pde7b', 'Arid1a', 'Pdgfd', 'Pdxp', 'Pdzph1', 'Arpp21', 'Peg3', 'Atp1a2', 'Atp1b2', 'Atp5g1', 'Atp5g2', 'Atp6v0c', 'Pitx2', 'Avp', 'Pkib', 'Pla2g10', 'B230323A14Rik', 'Basp1', 'Bbs1', 'Bc1', 'Ppia', 'Bsg', 'Btg3', 'Prdx1', 'Camk2a', 'Camk2n1', 'Ptprd', 'Ralyl', 'Rasgrp2', 'Cd24a', 'Cd81', 'Cd9', 'Resp18', 'Cdk8', 'Cebpb', 'Rmst', 'Rnf165', 'Chchd10', 'Ckb', 'Rtn1', 'Rubie', 'Rundc3a', 'Clic3', 'Samd12', 'Cmss1', 'Sap30bpos', 'Cntn1', 'Cops9', 'Selenop', 'Sema3c', 'Cox4i1', 'Cox6c', 'Cox7a2', 'Sema6d', 'Cox8a', 'Cpe', 'Sept4', 'Sept7', 'Sfrp1', 'Sgip1', 'Crym', 'Cst3', 'Ctnna2', 'Shisa6', 'Six3', 'Cycs', 'Cyfip2', 'Slc12a5', 'Slc17a6', 'Dbp', 'Dcc', 'Dcn', 'Slc6a11', 'Slc6a7', 'Slco2a1', 'Slit2', 'Dlg2', 'Dmp1', 'Dmxl1', 'Snhg3', 'Sorbs1', 'Dpp10', 'Dpp6', 'Sparcl1', 'Dync1i1', 'Dynll1', 'Spock1', 'Spock3', 'Eef1d', 'Eef1g', 'Ssbp4', 'Egfem1', 'Egr1', 'Eif1', 'Elmod1', 'Emb', 'Eno1', 'Sulf2', 'Swi5', 'Fabp5', 'Fabp7', 'Fam20a', 'Far1os', 'Fbln5', 'Thrsp', 'Timp3', 'Fgfr3', 'Tmem173', 'Fos', 'Foxg1', 'Fth1', 'Tmsb4x', 'Gadd45g', 'Gas5', 'Gfap', 'Tril', 'Gjc3', 'Gnas', 'Trpm3', 'Tsc22d3', 'Tsix', 'Gpc5', 'Gpc6', 'Tspan7', 'Ttc3', 'Tubb2a', 'Gria1', 'Gria2', 'Uba52', 'Gstm1', 'Uckl1os', 'H3f3b', 'Usp29', 'Hes5', 'Vim', 'Vsnl1', 'Hspa8', 'Xist', 'Id2', 'Ier2', 'Ywhaz', 'Igf2', 'Zfas1', 'Iqub', 'Irak2', 'Itih3', 'Itm2c', 'Jun', 'Kcnip4', 'Zwint', 'Klc1', 'Lars2', 'Ldhb', 'Lgi1', 'Lhfp', 'Lix1']

/opt/python/3.8.8/lib/python3.8/site-packages/sklearn/svm/_base.py:299: ConvergenceWarning:

Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.

Logit

# Generate labels for whole hypothalamus reference dataset
predictions_logit_PRJNA548917 = subregional_model.predict(df_eval2)
adata_eval2.obs["predict_logit_subregion"] = adata_eval2.obs["condit"]
adata_eval2.obs["predict_logit_subregion"] = predictions_logit_PRJNA548917
adata_eval2.obs["predict_logit_subregion"] = pd.Categorical(
    values=adata_eval2.obs["predict_logit_subregion"].astype(str),
    categories=natsorted(
        map(str, np.unique(adata_eval2.obs["predict_logit_subregion"].astype(str)))
    ),
)

PRJNA548917_regulons.obs["predict_logit_subregion"] = adata_eval2.obs["predict_logit_subregion"]

adata_eval2.obsm["X_umap_subfunct"] = adata_eval2.obsm["X_umap"]

reducer_PRJNA548917 = UMAP(
    n_neighbors=ks,
    n_epochs=1000,
    init="spectral",
    learning_rate=0.1,
    min_dist=min_dist,
    spread=spread,
    repulsion_strength=2.0,
    negative_sample_rate=negative_sample_rate,
    densmap=True,
    n_components=2,
    random_state=reseed,
    verbose=False,
)
# reducer_PRJNA548917.fit(
#     adata_eval2.obsm["X_aucell"], adata_eval2.obs["predict_logit_subregion"].astype(str)
# )
reducer_PRJNA548917.fit(
    adata_eval2.to_df().loc[:, X_uni_to_PRJNA548917.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist()], adata_eval2.obs["predict_logit_subregion"].astype(str)
)

UMAP(densmap=True, learning_rate=0.1, min_dist=1.0, n_epochs=1000, n_neighbors=20, negative_sample_rate=10, random_state=42, repulsion_strength=2.0, spread=2, tqdm_kwds={'bar_format': '{desc}: {percentage:3.0f}%| {bar} {n_fmt}/{total_fmt} [{elapsed}]', 'desc': 'Epochs completed', 'disable': True})

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# adata_eval2.obsm["X_umap_logit"] = reducer_PRJNA548917.transform(adata_eval2.obsm["X_aucell"])
adata_eval2.obsm["X_umap_logit"] = reducer_PRJNA548917.transform(adata_eval2.to_df().loc[:, X_uni_to_PRJNA548917.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist()])
PRJNA548917_regulons.obsm["X_umap_logit"] = adata_eval2.obsm["X_umap_logit"]

adata_eval2.write(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA548917-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)
adata_eval2.write_loom(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA548917-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.loom"
)
pd.DataFrame(adata_eval2.obsm["X_umap_logit"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA548917-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-logit.tsv",
    sep="\t",
    header=True,
)
pd.DataFrame(adata_eval2.obsm["X_umap_subfunct"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/PRJNA548917-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-subfunct.tsv",
    sep="\t",
    header=True,
)

The loom file will lack these fields:
{'X_pca', 'ora_pvals', 'X_aucell', 'X_umap', 'ora_estimate', 'X_umap_logit', 'X_pacmap', 'X_umap_subfunct'}
Use write_obsm_varm=True to export multi-dimensional annotations

save_PRJNA548917 = f"-best_logit-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pdf"

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=["predict_logit_subregion", "k_tree"],
    title=f"UMAP supervised subfunction: PRJNA548917 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save=save_PRJNA548917)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-best_logit-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=["predict_logit_subregion", "k_tree"],
    title=f"UMAP supervised density logit prediction: PRJNA548917 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-predict_logit_subregion" + save_PRJNA548917,
)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-predict_logit_subregion-best_logit-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

XGBoost

# Generate labels for whole hypothalamus reference dataset
predictions_xgboost_PRJNA548917 = subregional_model2.predict(df_eval2)
adata_eval2.obs["predict_xgboost_subregion"] = adata_eval2.obs["condit"]
adata_eval2.obs["predict_xgboost_subregion"] = predictions_xgboost_PRJNA548917
adata_eval2.obs["predict_xgboost_subregion"] = pd.Categorical(
    values=adata_eval2.obs["predict_xgboost_subregion"].astype(str),
    categories=natsorted(
        map(str, np.unique(adata_eval2.obs["predict_xgboost_subregion"].astype(str)))
    ),
)

PRJNA548917_regulons.obs["predict_xgboost_subregion"] = adata_eval2.obs["predict_xgboost_subregion"]

reducer_PRJNA548917 = UMAP(
    n_neighbors=ks,
    n_epochs=1000,
    init="spectral",
    learning_rate=0.1,
    min_dist=min_dist,
    spread=spread,
    repulsion_strength=2.0,
    negative_sample_rate=negative_sample_rate,
    densmap=True,
    n_components=2,
    random_state=reseed,
    verbose=False,
)
# reducer_PRJNA548917.fit(
#     adata_eval2.obsm["X_aucell"],
#     adata_eval2.obs["predict_xgboost_subregion"].astype(str)
# )

reducer_PRJNA548917.fit(
    adata_eval2.to_df().loc[:, X_uni_to_PRJNA548917.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist()], adata_eval2.obs["predict_xgboost_subregion"].astype(str)
)

UMAP(densmap=True, learning_rate=0.1, min_dist=1.0, n_epochs=1000, n_neighbors=20, negative_sample_rate=10, random_state=42, repulsion_strength=2.0, spread=2, tqdm_kwds={'bar_format': '{desc}: {percentage:3.0f}%| {bar} {n_fmt}/{total_fmt} [{elapsed}]', 'desc': 'Epochs completed', 'disable': True})

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# adata_eval2.obsm["X_umap"] = reducer_PRJNA548917.transform(adata_eval2.obsm["X_aucell"])
adata_eval2.obsm["X_umap"] = reducer_PRJNA548917.transform(adata_eval2.to_df().loc[:, X_uni_to_PRJNA548917.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist()])
PRJNA548917_regulons.obsm["X_umap_xgboost"] = adata_eval2.obsm["X_umap"]

adata_eval2.write(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA548917-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)
adata_eval2.write_loom(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA548917-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.loom"
)
pd.DataFrame(adata_eval2.obsm["X_umap"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA548917-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-xgboost.tsv",
    sep="\t",
    header=True,
)

The loom file will lack these fields:
{'X_pca', 'ora_pvals', 'X_aucell', 'X_umap', 'ora_estimate', 'X_umap_logit', 'X_pacmap', 'X_umap_subfunct'}
Use write_obsm_varm=True to export multi-dimensional annotations

save_PRJNA548917 = f"-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pdf"

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=["predict_xgboost_subregion", "k_tree"],
    title=f"UMAP supervised subfunction: PRJNA548917 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save=save_PRJNA548917,
)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.umap(
    adata_eval2,
    color=["predict_xgboost_subregion"],
    title=f"UMAP: supervised density XGBoost, PRJNA548917 \n(amb.FPR={cb_fpr}, k={k}, MSP={connectivity_model}, \nmetric={metric}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-predict_xgboost_subregion" + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-predict_xgboost_subregion-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

npepdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in npep if g in adata_eval2.var_names])]
npepdf.describe()
npepcols = [col for col in npepdf.columns if npepdf[col].quantile(0.985) > 1]

for gene in npepcols:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        vmax="p98.5",
        na_color="#9A9FB080",
        save="-npep-" + gene + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npep-Reln-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

nprdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in npr if g in adata_eval2.var_names])]
nprdf.describe()

	Adcyap1r1	Avpr1a	Calcr	Calcrl	Cckar	Cckbr	Cntfr	Crhr1	Crhr2	Esr1	...	Sstr1	Sstr2	Sstr3	Tacr1	Tacr3	Trhr	Trhr2	Tshr	Vipr1	Vipr2
count	3042.000000	3042.0	3042.000000	3042.000000	3042.0	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	...	3042.0	3042.000000	3042.0	3042.000000	3042.000000	3042.000000	3042.0	3042.000000	3042.0	3042.000000
mean	0.534188	0.0	0.000986	0.001972	0.0	0.001972	0.139053	0.000329	0.000329	0.000986	...	0.0	0.001644	0.0	0.001644	0.001644	0.000329	0.0	0.005260	0.0	0.001972
std	0.792713	0.0	0.031393	0.044375	0.0	0.051253	0.390693	0.018131	0.018131	0.031393	...	0.0	0.040515	0.0	0.040515	0.040515	0.018131	0.0	0.076756	0.0	0.044375
min	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000
25%	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000
50%	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000
75%	1.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000
max	5.000000	0.0	1.000000	1.000000	0.0	2.000000	3.000000	1.000000	1.000000	1.000000	...	0.0	1.000000	0.0	1.000000	1.000000	1.000000	0.0	2.000000	0.0	1.000000

8 rows × 67 columns

nprdf.quantile(q=[0.5, 0.9, 0.985, 1.0], axis=0)

	Adcyap1r1	Avpr1a	Calcr	Calcrl	Cckar	Cckbr	Cntfr	Crhr1	Crhr2	Esr1	...	Sstr1	Sstr2	Sstr3	Tacr1	Tacr3	Trhr	Trhr2	Tshr	Vipr1	Vipr2
0.500	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.900	2.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.985	3.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1.000	5.0	0.0	1.0	1.0	0.0	2.0	3.0	1.0	1.0	1.0	...	0.0	1.0	0.0	1.0	1.0	1.0	0.0	2.0	0.0	1.0

4 rows × 67 columns

nprcols = [col for col in nprdf.columns if nprdf[col].quantile(0.985) > 1]
nprcols3 = [col for col in nprdf.columns if col not in nprcols and nprdf[col].max() == 1]
nprcols2 = [col for col in nprdf.columns if col not in nprcols and col not in nprcols3]
print(nprcols)
print(nprcols2)
print(nprcols3)

['Adcyap1r1', 'Ghr', 'Igf1r', 'Insr', 'Ntrk2', 'Ntsr2']
['Avpr1a', 'Cckar', 'Cckbr', 'Cntfr', 'Galr2', 'Galr3', 'Ghsr', 'Glp1r', 'Gpr55', 'Gpr83', 'Grpr', 'Hcrtr2', 'Insrr', 'Mc1r', 'Mc3r', 'Npffr1', 'Npffr2', 'Npr2', 'Npy2r', 'Npy5r', 'Oprk1', 'Oprm1', 'Prlhr', 'Prlr', 'Prokr2', 'Qrfpr', 'Rxfp1', 'Rxfp2', 'Sstr1', 'Sstr3', 'Trhr2', 'Tshr', 'Vipr1']
['Calcr', 'Calcrl', 'Crhr1', 'Crhr2', 'Esr1', 'Galr1', 'Gpr149', 'Hcrtr1', 'Kiss1r', 'Lepr', 'Mc4r', 'Mchr1', 'Nmbr', 'Nmur1', 'Nmur2', 'Npr1', 'Npr3', 'Npsr1', 'Npy1r', 'Ntsr1', 'Oprd1', 'Oprl1', 'Oxtr', 'Sstr2', 'Tacr1', 'Tacr3', 'Trhr', 'Vipr2']

sc.pl.umap(
    adata_eval2,
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nprcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-npr2" + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr2-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nprcols3,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    na_color="#9A9FB080",
    save="-npr3" + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr3-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

nmrdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in nmr if g in adata_eval2.var_names])]
nmrdf.describe()

	Adra1a	Adra1b	Adra2a	Adra2b	Adra2c	Adrb1	Adrb2	Adrb3	Adrm1	Adora1	...	P2rx7	P2ry1	P2ry2	P2ry6	P2ry12	P2ry13	P2ry14	Ryr1	Ryr2	Ryr3
count	3042.000000	3042.000000	3042.000000	3042.0	3042.0	3042.000000	3042.0	3042.0	3042.000000	3042.000000	...	3042.000000	3042.000000	3042.000000	3042.0	3042.000000	3042.0	3042.000000	3042.000000	3042.000000	3042.000000
mean	0.057528	0.010191	0.083169	0.0	0.0	0.030901	0.0	0.0	0.035832	0.107824	...	0.060158	0.007232	0.000329	0.0	0.000657	0.0	0.032873	0.001315	0.048652	0.003616
std	0.271968	0.103672	0.293500	0.0	0.0	0.176836	0.0	0.0	0.189406	0.335666	...	0.252571	0.084748	0.018131	0.0	0.025637	0.0	0.185563	0.044399	0.246514	0.060035
min	0.000000	0.000000	0.000000	0.0	0.0	0.000000	0.0	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.0	0.0	0.000000	0.0	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.0	0.0	0.000000	0.0	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.000000	0.000000	0.0	0.0	0.000000	0.0	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
max	5.000000	2.000000	3.000000	0.0	0.0	2.000000	0.0	0.0	2.000000	3.000000	...	2.000000	1.000000	1.000000	0.0	1.000000	0.0	2.000000	2.000000	3.000000	1.000000

8 rows × 134 columns

nmrcols = [col for col in nmrdf.columns if nmrdf[col].quantile(0.985) > 1]
nmrcols3 = [col for col in nmrdf.columns if col not in nmrcols and nmrdf[col].max() == 1]
nmrcols2 = [col for col in nmrdf.columns if col not in nmrcols and col not in nmrcols3]
print(nmrcols)
print(nmrcols2)
print(nmrcols3)

['Gria1', 'Gria2', 'Grid1', 'Grid2', 'Grik2', 'Grik5', 'Grin2b', 'Grin2c', 'Grin3a', 'Grm5', 'Grm3', 'Gabra2', 'Gabrb1', 'Gabrb3', 'Gabrg1', 'Gabrg3', 'Gabbr1', 'Gabbr2', 'Gnas', 'Gnai2', 'Gnao1', 'Gnaq', 'Gna13', 'Gnb1', 'Gnb2', 'Gng5', 'Gng12']
['Adra1a', 'Adra1b', 'Adra2a', 'Adra2b', 'Adra2c', 'Adrb1', 'Adrb2', 'Adrb3', 'Adrm1', 'Adora1', 'Adora2a', 'Adora2b', 'Adora3', 'Chrm2', 'Chrm5', 'Chrna1', 'Chrna2', 'Chrna7', 'Chrnb3', 'Gria3', 'Gria4', 'Grik1', 'Grik4', 'Grin1', 'Grm2', 'Grm4', 'Grm7', 'Grm8', 'Gabra1', 'Gabra4', 'Gabrb2', 'Gabre', 'Gabrq', 'Gabrr1', 'Drd3', 'Drd4', 'Htr1a', 'Htr1d', 'Htr2c', 'Htr3a', 'Htr3b', 'Htr4', 'Htr6', 'Htr7', 'Gnai1', 'Gnai3', 'Gna11', 'Gna12', 'Gnb3', 'Gnb4', 'Gnb5', 'Gng4', 'Gng7', 'Gng8', 'Gng10', 'Gng11', 'Gng13', 'P2rx1', 'P2rx2', 'P2rx4', 'P2rx6', 'P2rx7', 'P2ry6', 'P2ry13', 'P2ry14', 'Ryr1', 'Ryr2']
['Chrm1', 'Chrm3', 'Chrm4', 'Chrna3', 'Chrna4', 'Chrna5', 'Chrna6', 'Chrna9', 'Chrnb1', 'Chrnb2', 'Grik3', 'Grin2a', 'Grin2d', 'Grin3b', 'Grm1', 'Gabra3', 'Gabra5', 'Gabrg2', 'Gabrd', 'Gabrp', 'Gabrr2', 'Drd1', 'Drd2', 'Drd5', 'Htr1b', 'Htr1f', 'Htr2a', 'Htr2b', 'Htr5a', 'Htr5b', 'Gnal', 'Gng2', 'Gng3', 'Gngt2', 'P2rx3', 'P2rx5', 'P2ry1', 'P2ry2', 'P2ry12', 'Ryr3']

sc.pl.umap(
    adata_eval2,
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nmrcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-nmr2" + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr2-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nmrcols3,
    frameon=False,
    show=True,
    size=40,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    vmin=0.0,
    na_color="#9A9FB080",
    save="-nmr3" + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr3-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

adddf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in genes_embed if g in adata_eval2.var_names])]
adddf.describe()

	Abcd1	Abcd2	Abcd3	Acaa2	Acox1	Agt	Aldh1a1	Aldh1l1	Aldoc	Apoe	...	Slc4a4	Slc6a11	Slc7a10	Slc38a1	Slit1	Slit2	Sox9	Tafa1	Tkt	Trpv1
count	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	...	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000
mean	0.017423	0.191321	0.215319	0.060487	0.120316	4.180473	0.104865	0.342209	1.061144	27.824457	...	5.226167	5.102235	1.341880	0.063445	0.039448	0.450033	0.361275	0.089086	0.312295	0.000986
std	0.130862	0.455393	0.487923	0.255727	0.366271	3.639904	0.394615	0.629945	1.238247	20.039721	...	3.608441	3.507459	1.287948	0.390934	0.206174	0.989405	0.650287	0.379886	0.594649	0.031393
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	16.000000	...	3.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	0.000000	0.000000	1.000000	23.000000	...	5.000000	4.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.000000	0.000000	0.000000	0.000000	6.000000	0.000000	1.000000	2.000000	34.000000	...	7.000000	7.000000	2.000000	0.000000	0.000000	0.000000	1.000000	0.000000	1.000000	0.000000
max	1.000000	3.000000	4.000000	2.000000	4.000000	25.000000	4.000000	6.000000	10.000000	237.000000	...	31.000000	27.000000	9.000000	7.000000	3.000000	10.000000	5.000000	7.000000	4.000000	1.000000

8 rows × 78 columns

agcols = [col for col in adddf.columns if adddf[col].quantile(0.985) > 1]
agcols3 = [col for col in adddf.columns if col not in agcols and adddf[col].max() == 1]
agcols2 = [col for col in adddf.columns if col not in agcols and col not in agcols3]
print(agcols)
print(agcols2)
print(agcols3)

['Abcd2', 'Abcd3', 'Agt', 'Aldh1a1', 'Aldh1l1', 'Aldoc', 'Apoe', 'Aqp4', 'Ckb', 'Cst3', 'Dcc', 'Egfr', 'Eno1', 'Fgfr3', 'Fos', 'Fth1', 'Ftl1', 'Gfap', 'Gja1', 'Glul', 'Hacd2', 'Hepacam', 'Htra1', 'Lxn', 'Mfn1', 'Mgll', 'Ndrg2', 'Nfia', 'Pla2g7', 'Plcb1', 'Pygb', 'S100a6', 'S100b', 'Scd2', 'Sgcd', 'Slc1a2', 'Slc1a3', 'Slc4a4', 'Slc6a11', 'Slc7a10', 'Slit2', 'Sox9', 'Tkt']
['Acaa2', 'Acox1', 'Cnr2', 'Dagla', 'Daglb', 'Ech1', 'Fis1', 'Gli1', 'Hadhb', 'Lcat', 'Lpcat3', 'Mfn2', 'Mief1', 'Opa1', 'Otp', 'Pex1', 'Pex13', 'Pex14', 'Pex16', 'Pex2', 'Pex3', 'Pex6', 'Slc38a1', 'Slit1', 'Tafa1']
['Abcd1', 'Cnr1', 'Decr2', 'Dnm1', 'Faah', 'Napepld', 'Pex10', 'Pex12', 'Pex26', 'Trpv1']

sc.pl.umap(
    adata_eval2,
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=agcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-adgen2" + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen2-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=agcols3,
    frameon=False,
    show=True,
    size=40,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    vmin=0.0,
    na_color="#9A9FB080",
    save="-adgen3" + save_PRJNA548917,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen3-best_xgboost-subregional_supervised-PRJNA548917-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

tfdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in tf_genes if g in adata_eval2.var_names])]
tfdf.describe()

	Aff3	Alx1	Arid1a	Arid1b	Arid2	Arid3a	Arid3b	Arid3c	Arid4a	Arid4b	...	Caskin1	Gtf3c4	Gcdh	BC024139	Zfp398	Tcfl5	A630089N07Rik	Phf8	Zhx3	Zfp82
count	3042.000000	3042.0	3042.000000	3042.000000	3042.000000	3042.0	3042.000000	3042.0	3042.000000	3042.000000	...	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000	3042.000000
mean	0.311966	0.0	0.208744	0.290598	0.216634	0.0	0.009204	0.0	0.126890	0.258383	...	0.215648	0.013149	0.033202	0.001972	0.061473	0.000657	0.055556	0.085799	0.379356	0.009862
std	0.644442	0.0	0.469542	0.577761	0.482594	0.0	0.095513	0.0	0.364947	0.548506	...	0.468872	0.116783	0.182826	0.051253	0.249633	0.025637	0.249703	0.302683	0.683392	0.098833
min	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000
max	6.000000	0.0	4.000000	4.000000	4.000000	0.0	1.000000	0.0	3.000000	4.000000	...	3.000000	2.000000	2.000000	2.000000	2.000000	1.000000	2.000000	3.000000	7.000000	1.000000

8 rows × 1350 columns

tfdf.quantile(q=[0.5, 0.9, 0.985, 1.0], axis=0)

	Aff3	Alx1	Arid1a	Arid1b	Arid2	Arid3a	Arid3b	Arid3c	Arid4a	Arid4b	...	Caskin1	Gtf3c4	Gcdh	BC024139	Zfp398	Tcfl5	A630089N07Rik	Phf8	Zhx3	Zfp82
0.500	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.900	1.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	1.0	1.0	...	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
0.985	2.0	0.0	2.0	2.0	2.0	0.0	0.0	0.0	1.0	2.0	...	2.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	2.0	0.0
1.000	6.0	0.0	4.0	4.0	4.0	0.0	1.0	0.0	3.0	4.0	...	3.0	2.0	2.0	2.0	2.0	1.0	2.0	3.0	7.0	1.0

4 rows × 1350 columns

tfcols = [col for col in tfdf.columns if tfdf[col].quantile(0.9) > 3]
tfcols3 = [col for col in tfdf.columns if col not in tfcols and tfdf[col].max() == 1]
tfcols2 = [col for col in tfdf.columns if col not in tfcols and tfdf[col].max() > 1]

print(tfcols)
print(tfcols2)
print(tfcols3)

['Rora', 'Hdac8', 'Npas3', 'Nfia', 'Pbx1', 'Trps1', 'Zbtb20', 'Zfpm2', 'Nfib', 'Ank2']
['Aff3', 'Arid1a', 'Arid1b', 'Arid2', 'Arid4a', 'Arid4b', 'Arid5b', 'Bcl6', 'Bdp1', 'Btaf1', 'Bag1', 'Bcor', 'Brf1', 'Bach1', 'Bach2', 'Ctbp1', 'Ctbp2', 'C1d', 'Cebpb', 'Cebpd', 'Cebpg', 'Cebpz', 'Ctcf', 'Cnot3', 'Cops2', 'Crebbp', 'Dbp', 'Dpf2', 'Ddx5', 'Ddx3x', 'Ddit3', 'Drap1', 'Ep300', 'Ets2', 'E2f3', 'E2f4', 'E2f5', 'E2f6', 'E4f1', 'Elf1', 'Elf2', 'Elk1', 'Elk3', 'Elk4', 'Eaf2', 'Eid1', 'Erf', 'Ewsr1', 'Fosb', 'Fos', 'Gabpa', 'Gatad2b', 'Gli1', 'Gli2', 'Gli3', 'Glis2', 'Herc1', 'Jdp2', 'Kat2a', 'Kat2b', 'Kat5', 'Kat6a', 'Klf10', 'Klf12', 'Klf13', 'Klf15', 'Klf4', 'Klf7', 'Klf9', 'Kcnip3', 'Lpp', 'Lhx2', 'Mxd1', 'Mxi1', 'Mlx', 'Mecom', 'Mybbp1a', 'Maz', 'Max', 'Meis1', 'Meis2', 'Mdfi', 'Nkx2-1', 'Nkx6-2', 'Paxip1', 'Pou2f1', 'Pou3f2', 'Pou3f4', 'Pou6f1', 'Patz1', 'Pknox2', 'Pknox1', 'Rab11fip3', 'Rorb', 'Rest', 'Rybp', 'Rrn3', 'Arhgap35', 'Sertad1', 'Sertad2', 'Smyd1', 'Smyd3', 'Setdb1', 'Skil', 'Smad3', 'Smad5', 'Smad7', 'Smad9', 'Snw1', 'Spen', 'Sox1', 'Sox11', 'Sox13', 'Sox2', 'Sox21', 'Sox4', 'Sox5', 'Sox6', 'Sox8', 'Sox9', 'Sub1', 'Smarcal1', 'Smarca1', 'Smarca2', 'Smarca4', 'Smarca5', 'Smarcb1', 'Smarcc1', 'Smarcd1', 'Smarcd2', 'Smarcd3', 'Smarce1', 'Smarcad1', 'Son', 'Tbpl1', 'Tmf1', 'Taf1', 'Taf10', 'Taf11', 'Taf12', 'Taf2', 'Taf3', 'Taf4b', 'Taf6', 'Taf7', 'Taf9', 'Taf1a', 'Taf1c', 'Tead1', 'Tead2', 'Traf4', 'Tsc22d1', 'Snrpc', 'Wdr5', 'Wwtr1', 'Wwox', 'Xbp1', 'Ybx1', 'Yy1', 'Ascl1', 'Ascc1', 'Atf2', 'Atf3', 'Atf4', 'Atf5', 'Atf6', 'Atf7ip', 'Atf7', 'Ar', 'Aatf', 'Apex1', 'Arnt2', 'Arnt', 'Arntl', 'Atr', 'Atm', 'Atoh8', 'Relb', 'Bhlhe40', 'Bhlhe41', 'Bmp7', 'Bin1', 'Bptf', 'Brd4', 'Brd7', 'Abl1', 'Creb1', 'Creb3', 'Creb3l1', 'Creb3l4', 'Crem', 'Camta2', 'Cic', 'Ctnnb1', 'Cdc5l', 'Creg1', 'Clock', 'Cux1', 'Dmtf1', 'Ccnt1', 'Ccnt2', 'Cdk7', 'Cdk8', 'Cdk9', 'Cdkn1a', 'Ddb1', 'Ddb2', 'Dcp1a', 'Dtx1', 'Ebf1', 'Egr1', 'Egr2', 'Egr3', 'Egr4', 'Eed', 'Epas1', 'Ezh1', 'Ezh2', 'Esrrg', 'Etv1', 'Etv3', 'Etv4', 'Etv5', 'Foxd1', 'Foxg1', 'Foxj2', 'Foxk1', 'Foxo1', 'Foxo3', 'Foxp1', 'Foxp2', 'Foxp4', 'Fus', 'Gtf2h1', 'Gtf2h2', 'Gtf2h4', 'Gtf2ird1', 'Gtf2i', 'Gtf2b', 'Gtf3a', 'Gtf3c1', 'Gcm1', 'Gmeb1', 'Gmeb2', 'Grhl1', 'Hes5', 'Hes6', 'Hey1', 'Hsf1', 'Hsbp1', 'Hltf', 'Hlf', 'Hnrnpab', 'Hnrnpc', 'Hnrnpdl', 'Hnrnph2', 'Hnrnpk', 'Hnrnpl', 'Hnrnpll', 'Hnrnpu', 'Hmgb1', 'Hmgb2', 'Hmgn1', 'Hmgn3', 'Hdac1', 'Hdac10', 'Hdac2', 'Hdac3', 'Hdac4', 'Hdac5', 'Hdac6', 'Hdac7', 'Hdac9', 'Hcfc1', 'Hivep1', 'Hivep2', 'Hif1a', 'Id1', 'Id2', 'Id3', 'Id4', 'Itgb3bp', 'Irf1', 'Irf2bp1', 'Irf2', 'Irf3', 'Irf9', 'Jade1', 'Junb', 'Jund', 'Jun', 'Lztfl1', 'Lcor', 'Kdm5a', 'Kdm5b', 'Kdm5c', 'Kmt2a', 'Maml1', 'Maml3', 'Med1', 'Med12', 'Med14', 'Med15', 'Med17', 'Med21', 'Med23', 'Med6', 'Mnat1', 'Mtf1', 'Mtf2', 'Mta1', 'Mecp2', 'Mbd1', 'Mbd2', 'Mitf', 'Mapk14', 'Mapk3', 'Mapk7', 'Msx1', 'Myef2', 'Mzf1', 'Mllt3', 'Mllt6', 'Mef2a', 'Mef2b', 'Mef2d', 'Nrg1', 'Npas2', 'Notch1', 'Notch2', 'Notch3', 'Nfic', 'Nfix', 'Nfat5', 'Nfatc2', 'Nfatc3', 'Nfkb1', 'Nfkbia', 'Nfkbiz', 'Nfe2l1', 'Ncor1', 'Ncor2', 'Ncoa1', 'Ncoa2', 'Ncoa3', 'Ncoa4', 'Ncoa5', 'Ncoa6', 'Ncoa7', 'Nrip1', 'Nrip3', 'Nr1d1', 'Nr1d2', 'Nr1h2', 'Nr2c1', 'Nr2c2', 'Nr2e1', 'Nr2f1', 'Nr2f2', 'Nr2f6', 'Nr3c1', 'Nr3c2', 'Nr4a1', 'Nr4a2', 'Nr4a3', 'Nr6a1', 'Nsd1', 'Nrf1', 'Nfx1', 'Npm1', 'Olig1', 'Olig2', 'Otx2', 'Pax6', 'Prrx1', 'Prrx2', 'Ptch1', 'Per1', 'Per2', 'Per3', 'Ppara', 'Ppard', 'Pgs1', 'Plag1', 'Parp1', 'Parp12', 'Pabpn1', 'Pcbp1', 'Pcbp2', 'Pcbp4', 'Pbrm1', 'Pcgf6', 'Pqbp1', 'Polr2a', 'Polr2b', 'Polr2k', 'Pbx3', 'Pbx4', 'Pdcd7', 'Phb2', 'Preb', 'Psmc5', 'Pin1', 'Pias1', 'Pias2', 'Pias3', 'Pias4', 'Pura', 'Purb', 'Phtf1', 'Rreb1', 'Rbpj', 'Rfx1', 'Rfx2', 'Rfx4', 'Rfx5', 'Rfxank', 'Rfxap', 'Rel', 'Rb1', 'Rbbp4', 'Rbbp7', 'Rbl1', 'Rarb', 'Rxrb', 'Rpl7', 'Rpl7a', 'Ring1', 'Rnf14', 'Rnf2', 'Rnf25', 'Rnf4', 'Runx2', 'Runx1t1', 'Sall1', 'Sall2', 'Sall3', 'Safb', 'Safb2', 'Scmh1', 'Scml2', 'Stat1', 'Stat3', 'Stat5a', 'Stat5b', 'Six3', 'Sirt3', 'Ski', 'Snapc4', 'Satb1', 'Satb2', 'Syne1', 'Sra1', 'Srebf2', 'Srebf1', 'Strn', 'Ssrp1', 'Supt16', 'Supt4a', 'Supt5', 'Suz12', 'Tdg', 'Tox', 'Thra', 'Thrap3', 'Thrb', 'Trip11', 'Tef', 'Topors', 'Sp1', 'Sp3', 'Sp4', 'Tcf12', 'Tcf20', 'Tcf3', 'Tcf4', 'Tcf7l1', 'Tcf7l2', 'Tfcp2', 'Tfdp1', 'Tfdp2', 'Ttf1', 'Tada3', 'Sin3a', 'Sin3b', 'Tbl1x', 'Tle1', 'Tle2', 'Tle3', 'Tle4', 'Trp53', 'Trp63', 'Trrap', 'Mdm2', 'Mdm4', 'Tsn', 'Tgs1', 'Trim24', 'Trim25', 'Trim28', 'Trim30a', 'Trim33', 'Tsc2', 'Ubp1', 'Ubtf', 'Usf1', 'Usf2', 'Uaca', 'Mafb', 'Maff', 'Mafg', 'Mafk', 'Vps72', 'Vezf1', 'Vdr', 'Vhl', 'Zeb1', 'Zeb2', 'Zbtb16', 'Zbtb33', 'Zscan21', 'Zfp106', 'Zfp110', 'Zfp148', 'Zfp239', 'Zfp263', 'Zfp326', 'Zfp423', 'Zfp467', 'Zfp503', 'Zfp64', 'Zfp68', 'Zfp90', 'Zic1', 'Zic4', 'Zkscan17', 'Zmym2', 'Zhx1', 'Zhx2', 'Adnp', 'Aebp2', 'Ank1', 'Ankfy1', 'Atf1', 'Bcl6b', 'Brca1', 'Zfp36l1', 'Cbfb', 'Cbx3', 'Cbx4', 'Ccnk', 'Cdk2', 'Cdk4', 'Cdkn2d', 'Dach1', 'Dnmt1', 'Dnmt3a', 'Drg1', 'Ell', 'Ercc2', 'Ercc3', 'Etv6', 'Fah', 'Fem1a', 'Fem1b', 'Foxn2', 'Gas7', 'Mtpn', 'Baz1a', 'Hes1', 'Hey2', 'Foxj1', 'Hipk2', 'Hira', 'Hmgn2', 'Hmg20b', 'Hmgb3', 'Hsf2', 'Ifnar2', 'Ikbkb', 'Ikbkg', 'Ilf3', 'Invs', 'Klf2', 'Klf3', 'Hivep3', 'Ldb1', 'Ldb2', 'Lmo4', 'Maf', 'Mbd3', 'Mcm3', 'Mcm4', 'Mcm5', 'Mcm6', 'Mef2c', 'Rab8a', 'Men1', 'Mid1', 'Mllt10', 'Nab1', 'Naca', 'Ndn', 'Nedd8', 'Nfe2l2', 'Nfkbib', 'Nfkbil1', 'Nfyc', 'Nkx2-2', 'Notch4', 'Uhrf1', 'Sqstm1', 'Otx1', 'Pbx2', 'Pdcd11', 'Enpp2', 'Peg3', 'Phf2', 'Papola', 'Cnot7', 'Pou3f3', 'Ppargc1a', 'Scand1', 'Ppp3cb', 'Ppp5c', 'Prkar1a', 'Prox1', 'Psmc3', 'Ptma', 'Rab11b', 'Rab18', 'Rai1', 'Rbl2', 'Rfx3', 'Rnps1', 'Polr2c', 'Polr2j', 'Ruvbl2', 'Rxrg', 'Khdrbs1', 'Sap18', 'Ostf1', 'Siah2', 'Six5', 'Ighmbp2', 'Snrpb', 'Sox12', 'Stag1', 'Stat2', 'Strap', 'Taf1b', 'Tcea1', 'Phf1', 'Thrsp', 'Tial1', 'Tmpo', 'Tnfaip3', 'Dedd', 'Ppp1r13b', 'Tob1', 'Tsg101', 'Baz1b', 'Zfp1', 'Zfp2', 'Zfp207', 'Zfp35', 'Zfp36', 'Zfp37', 'Zfp40', 'Zfp41', 'Zfp46', 'Zfp52', 'Zfx', 'Ash2l', 'Bpnt1', 'Dido1', 'Dazap2', 'Ing1', 'Map3k12', 'Mapk1', 'Mapk8', 'Cops5', 'Sh3d19', 'Zfp275', 'Trp53bp1', 'Ing4', 'Limd1', 'Mga', 'Zfp292', 'Pdlim4', 'Zfp354c', 'Abt1', 'Fbxw7', 'Nrbf2', 'Lsm4', 'Keap1', 'Ppp2r1a', 'Ccnc', 'Ppp1r10', 'Carhsp1', 'Plrg1', 'Pla2g6', 'Psmd10', 'Hif3a', 'Rab11a', 'Ddx20', 'Deaf1', 'Lrrc6', 'Tcerg1', 'Heyl', 'Zfp386', 'Rbm14', 'Zfp113', 'Trip4', 'Rps6ka4', 'Usp49', 'Zfp112', 'Jmy', 'Hic2', 'Carm1', 'Foxb1', 'Sirt2', 'Nmi', 'Asb1', 'Asb3', 'Znrd1', 'Dmap1', 'Polr2e', 'Polr2l', 'Zmynd11', 'Mcm8', 'Ctnnbl1', 'Ccnh', 'Hmg20a', 'Ssbp2', 'Yaf2', 'Nrarp', 'Rnf141', 'Psmd9', 'Pfdn1', 'Zfp606', 'Xab2', 'Rabl3', 'Polr2g', 'Sec14l2', 'Sncaip', 'Gtf2e2', 'Zdhhc21', 'Sirt5', 'Phf5a', 'Gtf2f2', 'Tulp4', 'Cnot8', 'Ppp1r12b', 'Zfp219', 'Polr2i', 'Dpf3', 'Basp1', 'Polr3b', 'Arhgap17', 'Bbx', 'Zcchc8', 'Prdm16', 'Prdm5', 'Zdhhc1', '4931423N10Rik', 'Fmnl2', 'Pogk', 'Ing3', 'Ddx54', 'Phf10', 'Lsm11', 'Asb6', 'Zfp131', 'Ssbp3', 'Bclaf1', 'Zfp248', 'Wasl', 'Hbp1', 'Rnf38', 'Btbd11', 'Gtf2e1', 'Prr3', 'Acyp2', 'Asb5', 'Ssbp4', 'Lbh', 'Helz', 'Asb8', 'Brd8', 'Brpf1', '9130019O22Rik', 'Polr3h', 'Zfp319', 'Mynn', 'Gtf2a1', 'Sirt1', 'Dach2', 'Maged1', 'Ptges2', 'Zfp451', 'Taf13', 'Zfp282', 'Snapc2', 'Ankrd10', 'Rcor1', 'Tcf19', 'Bmyc', 'Chd4', 'Zdhhc15', 'Ankhd1', 'Uhrf2', 'Zfp91', 'Dek', 'Dbx2', 'Brdt', 'Pawr', 'Baz2a', 'Mta3', 'Ivns1abp', 'Ankrd6', 'Ubn1', 'Zfp287', 'Ash1l', 'Clp1', 'Ell2', 'Dot1l', 'Hmgcs1', 'Sirt7', 'Zfp180', '6720489N17Rik', 'Zswim4', 'L3mbtl2', 'Ugp2', 'Btf3', 'Zfp523', 'Trerf1', 'Zfp281', 'Pms1', 'Zdhhc5', 'Ebf4', 'Nkx2-4', 'Asxl1', 'Tgif2', 'Zfp334', 'Pbxip1', 'Ddx58', 'Glis1', 'Tardbp', 'Creb5', 'Ppp1r12c', 'Zfp592', 'Zfp553', 'Zfp612', 'Zfp426', 'Rab8b', 'Scap', 'Tceal1', 'Homez', 'Fem1c', 'Setbp1', 'Carf', 'Polr2h', 'Zfp277', 'Shprh', 'Phf12', 'Zbtb1', 'Caskin1', 'Gtf3c4', 'Gcdh', 'BC024139', 'Zfp398', 'A630089N07Rik', 'Phf8', 'Zhx3']
['Arid3b', 'Barx2', 'Bmi1', 'Cebpa', 'Cited2', 'Cited4', 'Ets1', 'E2f1', 'E2f2', 'E2f7', 'Elf4', 'Elf5', 'Eid2', 'Daxx', 'Fezf2', 'Fli1', 'Gata1', 'Gata2', 'Gata3', 'Gata4', 'Gsx1', 'Hmx2', 'Ikzf1', 'Irx3', 'Klf1', 'Klf16', 'Klf5', 'Lmx1b', 'Mycbp', 'Nab2', 'Pou2f2', 'Pou3f1', 'Prdm1', 'Rbak', 'Rcor2', 'Spdef', 'Sox10', 'Sox3', 'Sox7', 'Tal1', 'Tbx1', 'Tbx19', 'Tbx2', 'Tbx21', 'Tbx3', 'Tbx5', 'Tbx6', 'Taf6l', 'Taf7l', 'Taf8', 'Tead3', 'Tgif1', 'Txk', 'Snrpb2', 'Arx', 'Ahr', 'Atoh7', 'Blzf1', 'Cdc6', 'Ciita', 'Crxos', 'Cux2', 'Cdkn2a', 'Dlx5', 'Emx2', 'En1', 'Esr1', 'Esrra', 'Esrrb', 'Esx1', 'Foxa1', 'Foxc1', 'Foxf1', 'Foxo4', 'Foxo6', 'Foxp3', 'Foxq1', 'Fosl1', 'Fosl2', 'Fhl2', 'Gbx2', 'Gtf2a1l', 'Gadd45a', 'Hes7', 'Hsf4', 'Hhex', 'Hmga1', 'Hoxa1', 'Hesx1', 'Ifi204', 'Irf7', 'Lef1', 'Mnt', 'Med24', 'Mn1', 'Mta2', 'Msx2', 'Msc', 'Myt1', 'Myt1l', 'Myb', 'Mybl1', 'Mybl2', 'Myc', 'Mllt1', 'Mllt11', 'Myog', 'Npas1', 'Nfatc1', 'Nfatc4', 'Nfkb2', 'Nfkbie', 'Nfe2l3', 'Nfil3', 'Nufip1', 'Nr1h3', 'Nr1h5', 'Nfya', 'Nfyb', 'Onecut2', 'Ovol2', 'Pax1', 'Pax5', 'Pax8', 'Pitx2', 'Pitx3', 'Ppargc1b', 'Pparg', 'Plagl1', 'Plagl2', 'Pcgf2', 'Pgr', 'Pml', 'Pcbd1', 'Rsl1', 'Rhox8', 'Rax', 'Rara', 'Rarg', 'Rxra', 'Runx1', 'Runx3', 'Scx', 'Srf', 'Six4', 'Sim2', 'Snai2', 'Suv39h1', 'Suv39h2', 'Tcea2', 'Tcf15', 'Tcf7', 'Tfam', 'Tfap2b', 'Tfap4', 'Tfe3', 'Tle6', 'Tgfb1i1', 'Twist1', 'Mafa', 'Mycn', 'Rela', 'Vav1', 'Vax1', 'Zrsr1', 'Zc3h8', 'Zbtb14', 'Zbtb17', 'Zbtb7b', 'Zfy1', 'Zfp128', 'Zfp354a', 'Zfp422', 'Zfp472', 'Zfp60', 'Zfp668', 'Zik1', 'Zic2', 'Zic3', 'Zic5', 'Zfpm1', 'Zmynd10', 'Aebp1', 'Ahrr', 'Aip', 'Neurog2', 'Bard1', 'Camk4', 'Cbx2', 'Cdkn2b', 'Cdkn2c', 'Chgb', 'Dr1', 'Ebf2', 'Bcl11a', 'Gabpb1', 'Hcls1', 'Hlx', 'Hoxb6', 'Htr5a', 'Elavl2', 'Irf4', 'Irx1', 'Irx2', 'Lmo2', 'Lyl1', 'Ascl2', 'Mcm2', 'Mcm7', 'Mpl', 'Nfatc2ip', 'Musk', 'Pou2f3', 'Ppp1r1b', 'Rorc', 'Sim1', 'Six6', 'Zfp143', 'Tbp', 'Tbx15', 'Tcea3', 'Trip6', 'Zfp105', 'Zfp13', 'Zfp27', 'Zfp39', 'Zfp51', 'Zfp54', 'Zfp57', 'Zfp59', 'Zfp61', 'Zfp9', 'Zfp92', 'Zfp93', 'Zfp94', 'Zfp97', 'Nr0b2', 'Zfp146', 'Polr3e', 'Zfp354b', 'Hnf4g', 'Cbx8', 'Sirt6', 'Htatip2', 'Pdlim1', 'Irx5', 'Zfp108', 'Nupr1', 'Ruvbl1', 'Zfp235', 'Zfp111', 'Zfp109', 'Fhl5', 'Shank3', 'Sap30', 'Sp5', 'Asb4', 'Fank1', 'Zdhhc6', 'Polr3d', 'Dedd2', 'Nudt12', 'Ankra2', 'Asb11', 'Zfp580', 'Trip13', '2810021J22Rik', 'Asb12', 'Zfp597', 'Fbxo24', 'Phf7', 'Nkd2', 'Nptxr', 'Zdhhc16', 'Cxxc1', 'Xrcc3', 'Zfp84', 'Hsf2bp', 'Zbtb3', 'Sirt4', '1700003F12Rik', 'Rai14', 'Snip1', 'Zfp142', '4930522L14Rik', 'Sp2', 'Abtb1', 'Zfp202', 'Sp6', 'Taf5l', 'Rab15', 'Lmo1', 'Lmx1a', 'Rem2', 'Zfp617', 'Sox30', 'Myocd', 'Polrmt', 'Gls2', 'Taf5', 'Ppp1r16b', 'Zfp189', 'Phf13', 'Hdac11', 'BC024063', 'Zfp454', 'St18', 'Dmrta1', 'Dmrta2', 'Obox3', 'Tcfl5', 'Zfp82']

for gene in tfcols:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        vmax="p90.0",
        na_color="#9A9FB080",
        save="-tf1-" + gene + save_PRJNA548917,
)

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for gene in tfcols2:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        na_color="#9A9FB080",
        save="-tf2" + gene + save_PRJNA548917,
)

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for gene in tfcols3:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
        vmin=0.0,
        na_color="#9A9FB080",
        save="-tf3" + gene + save_PRJNA548917,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA548917,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA548917,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA548917,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=tfcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-tf" + save_PRJNA548917,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA548917,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA548917,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA548917,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=tfcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-tf" + save_PRJNA548917,
)

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Kim et al 2020

PRJNA547712_counts = sc.read_h5ad(
    f"data/PRJNA547712/data/PRJNA547712-astrocytes_dataset-{cb_fpr}-scenic_plus.h5ad"
)

PRJNA547712_regulons = sc.read_h5ad(
    f"data/PRJNA547712/data/PRJNA547712-astrocytes_dataset-{cb_fpr}-regulons.h5ad"
)

adata_eval2 = PRJNA547712_counts.copy()
# sc.pp.filter_genes(adata_eval2, min_cells=5) # filter genes should be matched with training set
sc.pp.normalize_total(adata_eval2, target_sum=1e4)
sc.pp.log1p(adata_eval2)
sc.pp.regress_out(adata_eval2, ["nCount_RNA", "percent_mito_ribo"])

normalizing counts per cell

    finished (0:00:00)

regressing out ['nCount_RNA', 'percent_mito_ribo']

    sparse input is densified and may lead to high memory use

    finished (0:00:47)

df_eval2 = pd.DataFrame(
    adata_eval2.X, columns=adata_eval2.var_names, index=adata_eval2.obs_names
)

df_eval2 = df_eval2.loc[
    :, [g for g in uni_fs if g in adata_eval2.var_names]
]
df_eval2.head()

	0610005C13Rik	Lpl	Lpp	Lrat	Lrba	Lrch3	0610043K17Rik	Lrguk	Lrig1	1110008P14Rik	...	Lncpint	Lnpep	Lonrf2	Lor	Loxhd1	Loxl2	Lpar1	Lpar3	Lpgat1	Lpin2
SRR9214101_AAACCTGCACATCTTT-1	-0.009287	-0.004137	-0.617692	-0.008893	-0.245968	2.228593	-0.023126	-0.007112	-0.575424	-0.012432	...	-0.074332	-0.245344	-0.013107	-0.004211	0.0	-0.007314	0.005156	-0.002495	-0.001064	-0.088410
SRR9214101_AAACCTGGTTCGTTGA-1	-0.010421	-0.003937	-0.664592	-0.008489	-0.285805	-0.256290	-0.027767	-0.009176	-0.624083	-0.034261	...	-0.104037	1.927541	-0.032118	-0.005505	0.0	-0.006781	-0.003391	-0.002589	-0.007168	-0.108657
SRR9214101_AAACCTGTCGCCTGTT-1	-0.006975	-0.004965	-0.573128	-0.006016	-0.215370	-0.193904	-0.014102	-0.008244	-0.521240	-0.035125	...	-0.039206	-0.237089	-0.019368	-0.006285	0.0	-0.006580	0.003238	-0.001037	-0.003923	-0.070683
SRR9214101_AAACGGGCATTACCTT-1	-0.005895	-0.005520	1.876562	-0.003179	-0.221484	-0.202833	-0.010062	-0.010925	-0.514077	-0.072816	...	-0.033052	-0.262300	-0.040443	-0.009152	0.0	-0.005503	-0.005456	0.000154	-0.011428	-0.071898
SRR9214101_AAACGGGCATTCTCAT-1	-0.009481	-0.004252	-0.643965	-0.007503	-0.270874	-0.243597	-0.024079	-0.009373	1.667166	-0.040163	...	-0.088510	-0.277844	-0.032439	-0.006114	0.0	-0.006573	-0.003215	-0.002059	-0.007574	-0.100292

5 rows × 4163 columns

X_uni_to_PRJNA547712 = X_uni.loc[:, [g for g in uni_fs if g in adata_eval2.var_names]]
subregional_model.fit(X_uni_to_PRJNA547712, codes)

joblib.dump(
    subregional_model,
    f"models/svm/best-logit-subregional_PRJNA547712_astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle",
)

print(X_uni_to_PRJNA547712.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist())

['0610005C13Rik', '0610043K17Rik', '1190005I06Rik', 'Lrp8os2', '1500004A13Rik', 'Lrrc18', '1500015L24Rik', '1600010M07Rik', '1600012H06Rik', 'Lrrc45', 'Lrrc57', '1700003D09Rik', '1700003G18Rik', '1700003M07Rik', '1700008C04Rik', 'Lrrd1', 'Lrrk1', 'Lsm8', '1700022H01Rik', '1700024B05Rik', 'Ltbp1', '1700025F22Rik', '1700027I24Rik', '1700029I15Rik', '1700030F04Rik', 'Ly86', '1700030L22Rik', '1700030N03Rik', '1700034I23Rik', 'Lysmd4', 'M1ap', '1700054A03Rik', '1700060O08Rik', '1700061G19Rik', '1700064H15Rik', 'Mageb18', '1700081N11Rik', '1700084C06Rik', '1700095A13Rik', '1700095J03Rik', 'Mamdc2', 'Mamdc4', 'Maml3', '1700102P08Rik', '1700108F19Rik', 'Mannr', 'Maob', 'Map1b', '1700125G22Rik', 'Map2k4', 'Map3k11', '1810020O05Rik', '1810030O07Rik', 'Map4k3', 'Map7', '2010007H06Rik', '2210016F16Rik', '2310030A07Rik', '2310030G06Rik', '2310039L15Rik', 'Masp1', '2310068J16Rik', '2510009E07Rik', '2610001J05Rik', '2600006K01Rik', 'Mboat4', '2610307P16Rik', 'Mccc1os', '2610318N02Rik', '2700033N17Rik', '2700097O09Rik', 'Mcm6', '2810030D12Rik', '2810410L24Rik', 'Mcur1', 'Mdfi', '2810459M11Rik', '2900052N01Rik', 'Mdp1', '3110021N24Rik', '3110039M20Rik', '3110056K07Rik', '3200001D21Rik', '3222401L13Rik', 'Mef2c', 'Meg3', '4731419I09Rik', 'Meikin', 'Meis2', '4732496C06Rik', '4833422C13Rik', 'Metap1d', '4833423E24Rik', 'Mettl11b', 'Mettl17', '4921507P07Rik', '4921511C10Rik', 'Mettl24', '4921539H07Rik', '4930404I05Rik', '4930404N11Rik', 'Mfng', 'Mfsd14b', 'Mfsd2a', 'Mfsd3', '4930415C11Rik', '4930415O20Rik', 'Mfsd7a', 'Mgam', 'Mgam2-ps', '4930432B10Rik', '4930439D14Rik', '4930441H08Rik', '4930445B16Rik', '4930447C04Rik', 'Mid1', 'Miip', 'Mill2', '4930455B14Rik', '4930470G03Rik', 'Mir124-2hg', '4930473A02Rik', 'Mlh3', '4930512B01Rik', 'Mmp11', 'Mmrn2', '4930529K09Rik', 'Mogat1', '4930545L23Rik', 'Mok', '4930549C15Rik', 'Morn2', '4930554G24Rik', '4930554H23Rik', '4930555B11Rik', 'Morn5', '4930556G01Rik', '4930556J24Rik', 'Mpg', 'Mpped1', 'Mpped2', '4930579C12Rik', '4930589L23Rik', 'Mrgpra6', '4930594M22Rik.1', '4931403E22Rik', 'Mroh7', '4931413K12Rik', '4932411K12Rik', '4932416K20Rik', '4932435O22Rik', '4932443L11Rik', 'Mrpl42', '4933403O08Rik', '4933406F09Rik', 'Mrps18b', '4933408J17Rik', '4933421A08Rik', 'Msantd2', 'Msh6', '4933431E20Rik', 'Msl3', '4933431K23Rik', '4933433H22Rik', 'Mta1', 'Mta3', '5031434O11Rik', 'Mtcp1', '5033426O07Rik', 'Mtfp1', 'Mtg1', '5430416N02Rik', 'Mthfs', '5430431A17Rik', 'Mtmr4', '5730522E02Rik', '5830444B04Rik', 'Mttp', 'Mtus1', '5930435M05Rik', '6030498E09Rik', '6330403K07Rik', 'Mus81', 'Mvd', '6430503K07Rik', '6330576A10Rik', '6430553K19Rik', '6430628N08Rik', '6530402F18Rik', '6530409C15Rik', '6530411M01Rik', '6720427I07Rik', 'Mycbp', 'Mycl', '8030453O22Rik', 'Myh6', 'Mylip', '9130008F23Rik', '9130017K11Rik', '9130221H12Rik', 'Myo1f', 'Myoc', 'Myorg', '9330158H04Rik', 'Myrfl', 'Myrip', '9330199G10Rik', 'Naa10', '9630001P10Rik', '9630014M24Rik', 'Naip1', '9630028H03Rik', 'Nalcn', 'Napb', 'A1bg', 'A230051N06Rik', 'Nat8f7', 'Naxe', 'A230108P19Rik', 'A330076C08Rik', 'A330087D11Rik', 'A4galt', 'Ncoa6', 'Ncor2', 'A530053G22Rik', 'Ndnf', 'A630001O12Rik', 'A630012P03Rik', 'A730020M07Rik', 'A730049H05Rik', 'Ndufaf4', 'A730063M14Rik', 'A830035A12Rik', 'Necab3', 'Necap1', 'A930002C04Rik', 'Nectin1', 'A930006K02Rik', 'Nefh', 'Nek11', 'Nek8', 'AA465934', 'AB124611', 'Nell2', 'AC163638.2', 'AI429214', 'AI463229', 'Nfic', 'AI849053', 'Nfkbia', 'Nfkbib', 'Nfkbil1', 'AU023762', 'AW146154', 'Nhs', 'AY036118', 'Nifk', 'Aarsd1', 'Aasdhppt', 'Aass', 'Aatk', 'Nkain2', 'Nkd1', 'Nkx2-1', 'Nkx2-2', 'Nkx2-2os', 'Nkx2-4', 'Abcb1a', 'Abcb9', 'Nlgn2', 'Abcc10', 'Abcc2', 'Abcf2', 'Nme3', 'Abhd15', 'Nmnat2', 'Nmur1', 'Abl1', 'Ablim2', 'Abt1', 'Acap3', 'Notum', 'Acat2', 'Acat3', 'Ackr3', 'Ackr4', 'Acp1', 'Acsbg2', 'Nr1d1', 'Nr1d2', 'Nr2f2', 'Actr3b', 'Acvr1c', 'Acy3', 'Adam11', 'Nrg2', 'Adam5', 'Adamts12', 'Nsd2', 'Nsdhl', 'Nsf', 'Adamts2', 'Nsmce1', 'Nsmce3', 'Adat2', 'Ntn1', 'Ntn3', 'Ntng1', 'Nuak2', 'Adgrb2', 'Adgrf4', 'Adgrg2', 'Adgrl2', 'Nudt16l1', 'Adra1b', 'Adrb1', 'Nupr1l', 'Aff3', 'Afm', 'Agap2', 'Agbl1', 'Nxpe5', 'Oacyl', 'Ahctf1', 'Ahdc1', 'Ahsa1', 'Ogdhl', 'Ogfod2', 'Ogfod3', 'Ajm1', 'Olfml2b', 'Olfr1511', 'Akr1b3', 'Akt2', 'Olfr655', 'Olfr656', 'Olfr66', 'Olig2', 'Omg', 'Onecut2', 'Opa1', 'Alkal2', 'Alkbh7', 'Alpl', 'Alyref2', 'Amdhd2', 'Amer2', 'Ammecr1', 'Amot', 'Otoa', 'Amy1', 'Angel1', 'Otx1', 'Otx2', 'Otx2os1', 'Ovca2', 'Angptl4', 'Ankfn1', 'P2rx3', 'P2ry12', 'P2ry13', 'P2ry14', 'P2ry6', 'Ankrd22', 'P3h4', 'Pacsin1', 'Pacsin2', 'Padi2', 'Paf1', 'Anks1b', 'Ankzf1', 'Anln', 'Pank1', 'Paox', 'Anxa9', 'Aox1', 'Pard3bos1', 'Ap5s1', 'Apc2', 'Pawr', 'Pax5', 'Apln', 'Apmap', 'Apobec4', 'Aqp5', 'Pcdhb2', 'Aqp9', 'Ar', 'Pcdhb22', 'Pcgf1', 'Pck2', 'Arg2', 'Arhgap23', 'Pcsk2', 'Pcsk2os1', 'Arhgap31', 'Arhgap33', 'Arhgap40', 'Arhgap6', 'Pdcd1lg2', 'Arhgef10', 'Pde10a', 'Arhgef18', 'Arhgef28', 'Arhgef9', 'Arid3a', 'Pdgfb', 'Arl13a', 'Arl4d', 'Pdlim1', 'Armc2', 'Armcx4', 'Arntl', 'Pdzph1', 'Arpp21', 'Peak1os', 'Pebp4', 'Peg3', 'Arsj', 'Per2', 'Arx', 'Arvcf', 'Asb2', 'Pfkfb4', 'Ascl4', 'Asic1', 'Asic4', 'Asna1', 'Pglyrp1', 'Phactr2', 'Phf21b', 'Atic', 'Phldb2', 'Atoh7', 'Pi15', 'Atp23', 'Atp6v1e2', 'Atxn2l', 'Pitpnm3', 'Pitpnm2os2', 'Atxn7l2', 'Pitx2', 'Atxn7l3', 'Piwil4', 'Pkd1l2', 'Pkdcc', 'Pkhd1', 'Pkib', 'B130024G19Rik', 'Pla2g10', 'Pla2r1', 'Plat', 'Platr8', 'Plbd1', 'B430010I23Rik', 'Plcb3', 'Pld5', 'B830042I05Rik', 'Plek2', 'Plekha6', 'BC017158', 'BC034090', 'Plekho2', 'BC037032', 'Plin3', 'Plk2', 'BC050972', 'BC051226', 'Plppr5', 'Bach2os', 'Plut', 'Bag3', 'Bahcc1', 'Baiap2', 'Baiap3', 'Bambi', 'Plxnc1', 'Pmepa1', 'Barx2', 'Pmepa1os', 'Bbox1', 'Bbs1', 'Bcam', 'Bcar3', 'Pnpla2', 'Pnrc1', 'Bcas3os1', 'Podnl1', 'Pogk', 'Poglut1', 'Poglut2', 'Pold2', 'Bcl2l2', 'Polr1c', 'Becn1', 'Begain', 'Bet1l', 'Bicd2', 'Pou3f4', 'Bloc1s2', 'Bmp5', 'Bmp7', 'Bmx', 'Bnip2', 'Boc', 'Ppip5k2', 'Ppp1r12c', 'Ppp1r13b', 'Brd1', 'Ppp1r15a', 'Ppp1r16b', 'Brd3os', 'Ppp1r1b', 'Ppp1r26', 'Brip1os', 'Ppp1r3a', 'Ppp1r3d', 'Ppp1r9b', 'Ppp2r2c', 'Bsx', 'Btbd11', 'Btbd3', 'Btg2', 'Bves', 'Prag1', 'Prcd', 'C030007H22Rik', 'Prdm1', 'C030018K13Rik', 'C030032O16Rik', 'C030034L19Rik', 'Prdm8', 'Prdx4', 'Prelp', 'C1galt1c1', 'Prickle1', 'Prkaa2', 'C1s1', 'C2', 'Prkcb', 'Prkcd', 'C230034O21Rik', 'Prkcz', 'C2cd4c', 'C2cd4d', 'C330002G04Rik', 'C330013E15Rik', 'C4b', 'C530025M09Rik', 'C5ar1', 'C5ar2', 'C78334', 'Prox2', 'C8g', 'Prpf31', 'Cab39l', 'Prr11', 'Prrc2b', 'Cacna1h', 'Cacna2d2', 'Prrt1', 'Prrt2', 'Prss27', 'Prss36', 'Prss41', 'Prss45', 'Caln1', 'Calml4', 'Camk2a', 'Camk2b', 'Psme2b', 'Capsl', 'Ptch2', 'Ptchd4', 'Car15', 'Car3', 'Car8', 'Ptgs1', 'Pth1r', 'Carmil2', 'Casp6', 'Ptpn7', 'Catsperb', 'Ptprh', 'Catspere1', 'Ptpro', 'Cbfa2t3', 'Purg', 'Pygm', 'Ccdc134', 'Ccdc152', 'Ccdc162', 'Rab11fip4', 'Ccdc171', 'Rab20', 'Rab26os', 'Ccdc22', 'Ccdc24', 'Rab33b', 'Ccdc34os', 'Ccdc38', 'Ccdc84', 'Rabggtb', 'Ccdc85c', 'Ccdc86', 'Rad21l', 'Ccn1', 'Ralgapb', 'Ccn6', 'Ralyl', 'Rap1gap', 'Rapgef5', 'Cd164l2', 'Rasgrf1', 'Rasl11a', 'Rasl11b', 'Rassf2', 'Raver2', 'Rax', 'Rbis', 'Cd82', 'Rbm42', 'Cd96', 'Rbms2', 'Rbms3', 'Rbmxl1', 'Cdc42ep1', 'Rbpms', 'Cdc42ep4', 'Rbsn', 'Rcor2', 'Rcsd1', 'Cdh23', 'Recql5os1', 'Cdk10', 'Rfxap', 'Cebpb', 'Rgl2', 'Rgs2', 'Cenpt', 'Rhcg', 'Rhd', 'Rhobtb1', 'Rhobtb2', 'Ribc2', 'Rif1', 'Rimbp2', 'Ces5a', 'Ripor2', 'Cfap100', 'Cfap161', 'Rmi1', 'Rmnd5a', 'Cfap46', 'Cfap52', 'Rnd1', 'Cfap69', 'Rnf121', 'Cfap77', 'Rnf122', 'Cfb', 'Cfp', 'Rnf152', 'Chd5', 'Rnf24', 'Rnf5', 'Chil1', 'Robo2', 'Rogdi', 'Ror1', 'Ror2', 'Chrac1', 'Chrdl1', 'Rpe65', 'Chst3', 'Ciart', 'Cib1', 'Cic', 'Rrp15', 'Rsf1os1', 'Rsf1os2', 'Rslcan18', 'Cklf', 'Ckmt2', 'Rspo2', 'Clcn6', 'Rubie', 'Rufy2', 'Rundc3a', 'Clec3b', 'Clic3', 'Ryr1', 'Clic6', 'S100pbp', 'S1pr5', 'Sall3', 'Samd12', 'Sap30bpos', 'Sap30l', 'Cmtr2', 'Pigm', 'Cndp2', 'Cnnm3', 'Scg5', 'Cntn2', 'Cntnap1', 'Scly', 'Scpep1os', 'Scrib', 'Col1a1', 'Col1a2', 'Scrt1', 'Scube1', 'Scrt2', 'Col25a1', 'Col4a3', 'Sdk2', 'Sds', 'Col9a2', 'Colec12', 'Sec14l5', 'Selenbp1', 'Coq10b', 'Coq6', 'Corin', 'Coro2b', 'Sema3c', 'Sema3b', 'Sema3d', 'Sema4a', 'Sema3f', 'Cox6a2', 'Sema5a', 'Sema5b', 'Sema7a', 'Sept4', 'Cpne6', 'Serpinb5', 'Serpinb9', 'Serpine1', 'Serpine3', 'Cr2', 'Crebzf', 'Creld2', 'Sez6l', 'Sf3b4', 'Sfmbt1', 'Crlf2', 'Sfrp1', 'Crocc', 'Sft2d2', 'Sfxn2', 'Sgcz', 'Crybg2', 'Crym', 'Sgsm1', 'Sh2b1', 'Csmd2', 'Csrnp3', 'Sh3gl2', 'Sh3glb2', 'Cstdc2', 'Sh3rf2', 'Sh3rf3', 'Sh3tc2', 'Shank2', 'Ctif', 'Shh', 'Shisa3', 'Shisa6', 'Shmt1', 'Ctsh', 'Shmt2', 'Shprh', 'Shroom2', 'Sik1', 'Cutal', 'Sim2', 'Sipa1l1', 'Sipa1l3', 'Cxcr4', 'Sirt7', 'Six3', 'Six3os1', 'Cyb561', 'Cyb561d1', 'Skida1', 'Cyp2c55', 'Slc13a3', 'Cyp2c69', 'Slc13a5', 'Slc14a2', 'Slc15a1', 'Slc16a2', 'Cyp4f15', 'Slc17a9', 'Cyp51', 'Cyth1', 'Slc22a12', 'D030046N08Rik', 'Slc22a20', 'D030068K23Rik', 'Slc22a27', 'Slc22a3', 'D130079A08Rik', 'Slc24a4', 'Slc25a19', 'D630003M21Rik', 'Slc25a29', 'Slc25a47', 'Slc26a1', 'Slc26a2', 'Dach1', 'Dancr', 'Slc2a5', 'Dapk1', 'Dapk2', 'Slc30a4', 'Dbil5', 'Dcc', 'Dcdc2a', 'Dcdc2b', 'Slc38a1', 'Slc39a1', 'Ddias', 'Slc45a3', 'Slc46a3', 'Ddx3y', 'Slc5a11', 'Slc5a7', 'Defb39', 'Slc6a7', 'Slc7a2', 'Slc7a3', 'Deptor', 'Derl3', 'Dgat2', 'Slc9b1', 'Dgki', 'Dhcr24', 'Dhcr7', 'Slf1', 'Dhrs3', 'Slitrk2', 'Dhx58os', 'Slx1b', 'Diaph3', 'Dicer1', 'Smad3', 'Dio2', 'Diras2', 'Disc1', 'Dixdc1', 'Dkk3', 'Dlg4', 'Smg8', 'Dlk1', 'Smim22', 'Dlx2', 'Dmp1', 'Dmrta2', 'Smyd1', 'Dnaic1', 'Dnaja4', 'Snhg3', 'Snhg4', 'Snhg9', 'Dnajc27', 'Dnajc30', 'Dnd1', 'Sntg1', 'Doc2a', 'Snx20', 'Dock10', 'Dock11', 'Snx25', 'Dock5', 'Snx30', 'Dok6', 'Dok7', 'Dolpp1', 'Sod3', 'Sorcs3', 'Sowaha', 'Sox13', 'Sox4', 'Drc1', 'Sp5', 'Dusp1', 'Dusp27', 'Dusp6', 'Spatc1l', 'Specc1l', 'Spef2', 'Dynlt1b', 'Spg20', 'Dzank1', 'Spin4', 'Spink2', 'E030042O20Rik', 'E230032D23Rik', 'E230034D01Rik', 'Spsb4', 'Sqle', 'Ece1', 'Echdc2', 'Srcap', 'Srcin1', 'Srd5a3', 'Edrf1', 'Eef1akmt3', 'Eef1akmt4', 'Ssbp3', 'Ssbp4', 'Efcab10', 'Ssc4d', 'Sspo', 'Efcc1', 'Efhd1', 'Efhd2', 'Efna2', 'St3gal2', 'Eftud2', 'St6gal2', 'Egfem1', 'Egfl6', 'St6galnac3', 'St6galnac5', 'Egln3', 'Egr1', 'St8sia3os', 'Ehd2', 'Ehd3', 'Stab2', 'Stambpl1', 'Stat4', 'Stimate', 'Elavl4', 'Elfn2', 'Stk32a', 'Elmo1', 'Elmo2', 'Elmod1', 'Stk39', 'Elovl2', 'Stom', 'Elovl6', 'Emc4', 'Eml2', 'Stx1b', 'Emsy', 'Emx2', 'Emx2os', 'Enkd1', 'Enkur', 'Sulf2', 'Enpp2', 'Sult1a1', 'Sult2a6', 'Enthd1', 'Entpd3', 'Susd4', 'Epha3', 'Epha5', 'Svil', 'Ephb3', 'Swap70', 'Syn3', 'Eps8l1', 'Syne4', 'Syngap1', 'Synj1', 'Ercc3', 'Ercc6l2', 'Syt3', 'Sytl5', 'Esrrb', 'Ess2', 'Tada1', 'Taf13', 'Etv4', 'Etv5', 'Taf7l', 'Eva1b', 'Tafa1', 'Exoc2', 'Extl1', 'Eya1', 'Eya4', 'F2', 'Tbc1d10c', 'F5', 'F630206G17Rik', 'Fads3', 'Fam126b', 'Fam163a', 'Fam163b', 'Fam177a', 'Fam181a', 'Tdrd3', 'Fam193b', 'Tek', 'Fam228a', 'Fam229a', 'Fam3c', 'Fam43b', 'Fam57b', 'Tex22', 'Fam92b', 'Tex9', 'Far1os', 'Fas', 'Tgfbr3', 'Tgfbr3l', 'Tgif1', 'Fbll1', 'Fbln1', 'Fbln5', 'Fbp1', 'Fbxl14', 'Fbxo15', 'Thoc3', 'Ticam2', 'Fbxo9', 'Fcgr2b', 'Fcgr3', 'Fcrls', 'Fdft1', 'Fem1a', 'Tle2', 'Tlnrd1', 'Fgf10', 'Fgf13', 'Fgf20', 'Fgf5', 'Fgf9', 'Fgfr4', 'Tmcc2', 'Fhad1', 'Fhl1', 'Tmeff1', 'Fibp', 'Tmem100', 'Filip1l', 'Firre', 'Tmem123', 'Tmem125', 'Tmem132b', 'Tmem140', 'Flrt1', 'Flrt2', 'Flt3l', 'Flvcr1', 'Fmo1', 'Fn3krp', 'Tmem173', 'Fnbp4', 'Fndc5', 'Tmem181a', 'Tmem191c', 'Folr1', 'Folr2', 'Fos', 'Foxa1', 'Foxa3', 'Tmem204', 'Foxd1', 'Foxd2os', 'Foxg1', 'Tmem203', 'Tmem216', 'Tmem245', 'Frem1', 'Frmpd1os', 'Frmpd4', 'Tmem269', 'Frzb', 'Tmem37', 'Tmem44', 'Tmem50a', 'Fuca1', 'Fuca2', 'Tmem63a', 'Fut4', 'Tmem86a', 'Fzd10', 'Fzd10os', 'Fzd2', 'Fzd4', 'G0s2', 'G630016G05Rik', 'Tmprss6', 'G630093K05Rik', 'Gabra2', 'Tnfrsf19', 'Gabrr2', 'Tnfrsf26', 'Tnfsf12', 'Tnfsf4', 'Tnfsf8', 'Galnt10', 'Tnk2', 'Galnt2', 'Galnt5', 'Tnni3', 'Tns1', 'Gas1', 'Togaram2', 'Gas2l2', 'Tomm6os', 'Gata4', 'Gatc', 'Gatd3a', 'Tor1b', 'Tpd52l1', 'Gc', 'Gcnt1', 'Gcnt4', 'Gcnt7', 'Gdf10', 'Trac', 'Gdpd3', 'Gfm1', 'Traj27', 'Ggnbp1', 'Trcg1', 'Trdn', 'Gigyf1', 'Triap1', 'Gins4', 'Gjb2', 'Gjc2', 'Gjc3', 'Trim34b', 'Gldnos', 'Gle1', 'Gli1', 'Glipr1l3', 'Glipr2', 'Glmp', 'Trim8', 'Glrb', 'Trip6', 'Glt8d2', 'Trmu', 'Trp63', 'Trpm1', 'Gnmt', 'Gnpda2', 'Gnpnat1', 'Trpv2', 'Got2', 'Tshr', 'Tsix', 'Gpatch4', 'Gpc3', 'Tspan4', 'Tspoap1', 'Gpr141', 'Gpr146', 'Gpr153', 'Gpr155', 'Ttc12', 'Gpr162', 'Gpr37', 'Ttc34', 'Gpr87', 'Gprasp2', 'Gprc5c', 'Ttll5', 'Gpt2', 'Gprin2', 'Gpx8', 'Gramd4', 'Tubal3', 'Grid2ip', 'Grifin', 'Grik3', 'Grik5', 'Grin3a', 'Grin3b', 'Grm4', 'Uap1', 'Gssos1', 'Ubash3b', 'Gstt2', 'Gstz1', 'Ube4bos1', 'Ubfd1', 'Ubqln4', 'Ubtd1', 'Ubxn11', 'Gyg', 'Uckl1os', 'Ucma', 'H1f0', 'Uggt2', 'H2-K1', 'Umad1', 'H2-T23', 'Unc13c', 'Unc80', 'Hal', 'Upf1', 'Hap1', 'Hapln1', 'Hccs', 'Ush2a', 'Hdac4', 'Hdc', 'Hdhd5', 'Heg1', 'Usp6nl', 'Hemk1', 'Uty', 'Uvssa', 'Herpud1', 'Hesx1', 'Hexa', 'Heyl', 'Vash1', 'Vasn', 'Vasp', 'Hgh1', 'Higd1b', 'Higd1c', 'Vip', 'Hist1h2ad', 'Vmn2r15', 'Vmn2r29', 'Hist1h4j', 'Hist2h2aa1', 'Hmcn1', 'Hmgb2', 'Hmgcr', 'Hmgcs2', 'Vsig4', 'Hmgn5', 'Vstm4', 'Hnf4aos', 'Wars2', 'Hopx', 'Hoxa1', 'Hpcal1', 'Hpse', 'Hpse2', 'Hr', 'Hrh1', 'Wdr45', 'Hsd11b1', 'Wdr72', 'Wdr76', 'Wdr78', 'Wdr81', 'Hsd17b6', 'Hsf3', 'Hspa12a', 'Whamm', 'Wipf1', 'Wls', 'Wnt6', 'Wnt7a', 'Htr1f', 'Hyou1', 'Icosl', 'Id1', 'Ide', 'Idi1', 'Ier2', 'Yif1a', 'Ifi27', 'Ifi47', 'Yy2', 'Zap70', 'Igdcc3', 'Zbtb3', 'Igf2bp3', 'Igsf21', 'Zc3hav1', 'Il10rb', 'Zcwpw2', 'Il15', 'Il17c', 'Il1rapl2', 'Zfand2a', 'Il22ra1', 'Il31ra', 'Il33', 'Zfp110', 'Il6ra', 'Zfp157', 'Zfp160', 'Impdh2', 'Zfp217', 'Zfp263', 'Inhbc', 'Inka1', 'Ino80', 'Zfp319', 'Zfp330', 'Zfp335', 'Insig1', 'Zfp335os', 'Insyn2a', 'Zfp385a', 'Zfp414', 'Iqcc', 'Iqch', 'Zfp451', 'Iqub', 'Irak4', 'Zfp513', 'Zfp516', 'Irf7', 'Zfp524', 'Irs2', 'Zfp536', 'Irx3', 'Irx5', 'Zfp592', 'Ism1', 'Zfp629', 'Itgbl1', 'Zfp708', 'Itpka', 'Itpr1', 'Izumo4', 'Jag1', 'Zfp799', 'Zfp808', 'Zfp811', 'Jph2', 'Zfp85', 'Jpx', 'Zfp853', 'Jun', 'Junb', 'Kat2a', 'Zfp936', 'Kat8', 'Katnal2', 'Kbtbd12', 'Kcna2', 'Kcnc4', 'Zfp981', 'Kcng1', 'Kcng2', 'Kcng3', 'Kcng4', 'Zfp991', 'Kcnh3', 'Kcnip1', 'Kcnj3', 'Zic1', 'Zic2', 'Zic4', 'Zic3', 'Zim3', 'Kcnk5', 'Kcnq1', 'Kcns3', 'Kcnt1', 'Kctd12b', 'Kctd17', 'Kdm5d', 'Kdm8', 'Khdrbs3', 'Kif13b', 'Kif19a', 'Kif3c', 'Kifc5b', 'Kirrel2', 'Kiss1', 'Klc1', 'Klc2', 'Klhdc7a', 'Klhl10', 'Klhl25', 'Klre1', 'Krt8', 'Ky', 'L3hypdh', 'L3mbtl4', 'Lamb3', 'Lao1', 'Lars2', 'Lca5l', 'Lck', 'Lctl', 'Ldhc', 'Ldlr', 'Lenep', 'Leprot', 'Lgals12', 'Lgr6', 'Lhfpl2', 'Lhx2', 'Lipo3', 'Lmo3', 'Lmod1', 'Lonrf2', 'Loxhd1', 'Loxl2', 'Lpar1', 'Lpgat1', 'Lpin2']

subregional_model2.fit(X_uni_to_PRJNA547712, codes)

joblib.dump(
    subregional_model2,
    f"models/svm/best-xgboost-subregional_PRJNA547712_astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle",
)

print(X_uni_to_PRJNA547712.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist())

['Lrrc8a', 'Ltbp1', 'Luzp2', 'Malat1', 'Map3k5', 'Mbp', 'Mdga2', '2900097C17Rik', 'Medag', '4833423E24Rik', 'Mfge8', 'Mfsd2a', '4930447C04Rik', 'Msi2', 'Mt1', 'Mt2', 'Mt3', '5031439G07Rik', 'Myoc', 'Nap1l5', 'Nbl1', 'Ndn', 'Ndufa3', 'Nebl', 'Nefl', 'AA465934', 'AC149090.1', 'Nhs', 'AY036118', 'Nnat', 'Acot4', 'Actg1', 'Actn1', 'Nr2f2', 'Nrarp', 'Nrgn', 'Nrsn1', 'Nrxn3', 'Ntm', 'Adcy8', 'Nupr1', 'Agt', 'Ank3', 'Anln', 'Aplp1', 'Apod', 'Apoe', 'App', 'Aqp4', 'Pcsk1n', 'Pde10a', 'Arhgef28', 'Pde4d', 'Arid1a', 'Pdgfd', 'Pdxp', 'Pdzph1', 'Arpp21', 'Pgrmc1', 'Atp1b2', 'Atp5g1', 'Atp5md', 'Atp6v0c', 'Atpif1', 'Pitx2', 'Avp', 'Pkib', 'Pla2g10', 'B230323A14Rik', 'Basp1', 'Bc1', 'Ppia', 'Brinp3', 'Bsg', 'Bst1', 'Btg3', 'Prdx1', 'Camk2a', 'Camk2n1', 'Ptprd', 'Qk', 'Ralyl', 'Rasgrp2', 'Cd24a', 'Cd81', 'Cd9', 'Resp18', 'Cdk8', 'Cebpb', 'Rmst', 'Rnf165', 'Chchd10', 'Ckb', 'Rtn1', 'Rubie', 'Rundc3a', 'Clic3', 'Cmss1', 'Sap30bpos', 'Cntn1', 'Scg5', 'Cops9', 'Selenop', 'Sema3c', 'Cox4i1', 'Cox8a', 'Cpe', 'Sept4', 'Sept7', 'Serf2', 'Sfrp1', 'Sgip1', 'Crym', 'Cst3', 'Ctnna2', 'Shisa6', 'Six3', 'Six3os1', 'Cycs', 'Cyfip2', 'Slc17a6', 'Dbp', 'Dcc', 'Slc6a11', 'Slit2', 'Dmxl1', 'Dnah12', 'Son', 'Sorbs1', 'Dpp10', 'Sparcl1', 'Dync1i1', 'Dynll1', 'Spock1', 'Spock3', 'Ednrb', 'Eef1d', 'Eef1g', 'Efna5', 'Egfem1', 'Egr1', 'Eif1', 'Elmod1', 'Emb', 'Eno1', 'Sulf2', 'Swi5', 'Fabp5', 'Fabp7', 'Tceal9', 'Tenm1', 'Far1os', 'Fbln5', 'Thra', 'Thrsp', 'Timp3', 'Fgfr3', 'Tmem173', 'Fos', 'Fosb', 'Foxg1', 'Fth1', 'Tmsb4x', 'Gas5', 'Gfap', 'Tril', 'Gjc3', 'Gnas', 'Gng3', 'Trpm3', 'Tsc22d3', 'Tsix', 'Gpc5', 'Gpc6', 'Tspan7', 'Ttc3', 'Tubb2a', 'Gria1', 'Gria2', 'Uba52', 'Gstm1', 'Uckl1os', 'H3f3b', 'Uqcr11', 'Uqcrq', 'Usp29', 'Hes5', 'Vim', 'Hmgcs1', 'Hspa8', 'Xist', 'Id2', 'Ywhaz', 'Igf1r', 'Igf2', 'Iqub', 'Irak2', 'Itih3', 'Itm2c', 'Jaml', 'Jun', 'Junb', 'Kcnip4', 'Zic1', 'Zic2', 'Klc1', 'Lars2', 'Ldhb', 'Lgals3', 'Lgi1', 'Lix1']

/opt/python/3.8.8/lib/python3.8/site-packages/sklearn/svm/_base.py:299: ConvergenceWarning:

Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.

Logit

# Generate labels for whole hypothalamus reference dataset
predictions_logit_PRJNA547712 = subregional_model.predict(df_eval2)
adata_eval2.obs["predict_logit_subregion"] = adata_eval2.obs["condit"]
adata_eval2.obs["predict_logit_subregion"] = predictions_logit_PRJNA547712
adata_eval2.obs["predict_logit_subregion"] = pd.Categorical(
    values=adata_eval2.obs["predict_logit_subregion"].astype(str),
    categories=natsorted(
        map(str, np.unique(adata_eval2.obs["predict_logit_subregion"].astype(str)))
    ),
)

PRJNA547712_regulons.obs["predict_logit_subregion"] = adata_eval2.obs["predict_logit_subregion"]

adata_eval2.obsm["X_umap_subfunct"] = adata_eval2.obsm["X_umap"]

reducer_PRJNA547712 = UMAP(
    n_neighbors=ks,
    n_epochs=1000,
    init="spectral",
    learning_rate=0.1,
    min_dist=min_dist,
    spread=spread,
    repulsion_strength=2.0,
    negative_sample_rate=negative_sample_rate,
    densmap=True,
    n_components=2,
    random_state=reseed,
    verbose=False,
)
# reducer_PRJNA547712.fit(
#     adata_eval2.obsm["X_aucell"], adata_eval2.obs["predict_logit_subregion"].astype(str)
# )

reducer_PRJNA547712.fit(
    adata_eval2.to_df().loc[:, X_uni_to_PRJNA547712.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist()], adata_eval2.obs["predict_logit_subregion"].astype(str)
)

UMAP(densmap=True, learning_rate=0.1, min_dist=1.0, n_epochs=1000, n_neighbors=20, negative_sample_rate=10, random_state=42, repulsion_strength=2.0, spread=2, tqdm_kwds={'bar_format': '{desc}: {percentage:3.0f}%| {bar} {n_fmt}/{total_fmt} [{elapsed}]', 'desc': 'Epochs completed', 'disable': True})

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# adata_eval2.obsm["X_umap_logit"] = reducer_PRJNA547712.transform(adata_eval2.obsm["X_aucell"])
adata_eval2.obsm["X_umap_logit"] = reducer_PRJNA547712.transform(adata_eval2.to_df().loc[:, X_uni_to_PRJNA547712.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist()])
PRJNA547712_regulons.obsm["X_umap_logit"] = adata_eval2.obsm["X_umap_logit"]

adata_eval2.write(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA547712-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)
adata_eval2.write_loom(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA547712-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.loom"
)
pd.DataFrame(adata_eval2.obsm["X_umap_logit"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA547712-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-logit.tsv",
    sep="\t",
    header=True,
)
pd.DataFrame(adata_eval2.obsm["X_umap_subfunct"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/PRJNA547712-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-subfunct.tsv",
    sep="\t",
    header=True,
)

The loom file will lack these fields:
{'X_pca', 'ora_pvals', 'X_aucell', 'X_umap', 'ora_estimate', 'X_umap_logit', 'X_pacmap', 'X_umap_subfunct'}
Use write_obsm_varm=True to export multi-dimensional annotations

save_PRJNA547712 = f"-best_logit-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pdf"

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=["predict_logit_subregion", "k_tree"],
    title=f"UMAP supervised subfunction: PRJNA547712 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save=save_PRJNA547712)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-best_logit-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=["predict_logit_subregion", "k_tree"],
    title=f"UMAP supervised density logit prediction: PRJNA547712 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-predict_logit_subregion" + save_PRJNA547712,
)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-predict_logit_subregion-best_logit-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

XGBoost

# Generate labels for whole hypothalamus reference dataset
predictions_xgboost_PRJNA547712 = subregional_model2.predict(df_eval2)
adata_eval2.obs["predict_xgboost_subregion"] = adata_eval2.obs["condit"]
adata_eval2.obs["predict_xgboost_subregion"] = predictions_xgboost_PRJNA547712
adata_eval2.obs["predict_xgboost_subregion"] = pd.Categorical(
    values=adata_eval2.obs["predict_xgboost_subregion"].astype(str),
    categories=natsorted(
        map(str, np.unique(adata_eval2.obs["predict_xgboost_subregion"].astype(str)))
    ),
)

PRJNA547712_regulons.obs["predict_xgboost_subregion"] = adata_eval2.obs["predict_xgboost_subregion"]

reducer_PRJNA547712 = UMAP(
    n_neighbors=ks,
    n_epochs=1000,
    init="spectral",
    learning_rate=0.1,
    min_dist=min_dist,
    spread=spread,
    repulsion_strength=2.0,
    negative_sample_rate=negative_sample_rate,
    densmap=True,
    n_components=2,
    random_state=reseed,
    verbose=False,
)
# reducer_PRJNA547712.fit(
#     adata_eval2.obsm["X_aucell"],
#     adata_eval2.obs["predict_xgboost_subregion"].astype(str)
# )

reducer_PRJNA547712.fit(
    adata_eval2.to_df().loc[:, X_uni_to_PRJNA547712.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist()], adata_eval2.obs["predict_xgboost_subregion"].astype(str)
)

UMAP(densmap=True, learning_rate=0.1, min_dist=1.0, n_epochs=1000, n_neighbors=20, negative_sample_rate=10, random_state=42, repulsion_strength=2.0, spread=2, tqdm_kwds={'bar_format': '{desc}: {percentage:3.0f}%| {bar} {n_fmt}/{total_fmt} [{elapsed}]', 'desc': 'Epochs completed', 'disable': True})

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# adata_eval2.obsm["X_umap"] = reducer_PRJNA547712.transform(adata_eval2.obsm["X_aucell"])
adata_eval2.obsm["X_umap"] = reducer_PRJNA547712.transform(adata_eval2.to_df().loc[:, X_uni_to_PRJNA547712.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist()])
PRJNA547712_regulons.obsm["X_umap_xgboost"] = adata_eval2.obsm["X_umap"]

adata_eval2.write(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA547712-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)
adata_eval2.write_loom(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA547712-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.loom"
)
pd.DataFrame(adata_eval2.obsm["X_umap"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA547712-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-xgboost.tsv",
    sep="\t",
    header=True,
)

The loom file will lack these fields:
{'X_pca', 'ora_pvals', 'X_aucell', 'X_umap', 'ora_estimate', 'X_umap_logit', 'X_pacmap', 'X_umap_subfunct'}
Use write_obsm_varm=True to export multi-dimensional annotations

save_PRJNA547712 = f"-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pdf"

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=["predict_xgboost_subregion", "k_tree"],
    title=f"UMAP supervised subfunction: PRJNA547712 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save=save_PRJNA547712,
)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.umap(
    adata_eval2,
    color=["predict_xgboost_subregion"],
    title=f"UMAP: supervised density XGBoost, PRJNA547712 \n(amb.FPR={cb_fpr}, k={k}, MSP={connectivity_model}, \nmetric={metric}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-predict_xgboost_subregion" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-predict_xgboost_subregion-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

npepdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in npep if g in adata_eval2.var_names])]
npepdf.describe()
npepcols = [col for col in npepdf.columns if npepdf[col].quantile(0.985) > 1]

for gene in npepcols:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        vmax="p98.5",
        na_color="#9A9FB080",
        save="-npep-" + gene + save_PRJNA547712,
)

nprdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in npr if g in adata_eval2.var_names])]
nprdf.describe()

	Adcyap1r1	Avpr1a	Calcr	Calcrl	Cckar	Cckbr	Cntfr	Crhr1	Crhr2	Esr1	...	Rxfp2	Sstr1	Sstr2	Sstr3	Tacr1	Tacr3	Trhr	Tshr	Vipr1	Vipr2
count	3215.000000	3215.0	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	...	3215.000000	3215.000000	3215.000000	3215.0	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000
mean	0.483048	0.0	0.000622	0.008398	0.000622	0.001555	0.158009	0.000622	0.000622	0.008709	...	0.000622	0.003733	0.004355	0.0	0.000622	0.007154	0.003110	0.009953	0.000622	0.000311
std	0.786639	0.0	0.024938	0.097851	0.024938	0.039412	0.412066	0.024938	0.024938	0.099401	...	0.024938	0.065894	0.065856	0.0	0.024938	0.087905	0.055693	0.105366	0.024938	0.017636
min	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	1.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	11.000000	0.0	1.000000	2.000000	1.000000	1.000000	4.000000	1.000000	1.000000	2.000000	...	1.000000	2.000000	1.000000	0.0	1.000000	2.000000	1.000000	2.000000	1.000000	1.000000

8 rows × 66 columns

nprdf.quantile(q=[0.5, 0.9, 0.985, 1.0], axis=0)

	Adcyap1r1	Avpr1a	Calcr	Calcrl	Cckar	Cckbr	Cntfr	Crhr1	Crhr2	Esr1	...	Rxfp2	Sstr1	Sstr2	Sstr3	Tacr1	Tacr3	Trhr	Tshr	Vipr1	Vipr2
0.500	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.900	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.985	3.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1.000	11.0	0.0	1.0	2.0	1.0	1.0	4.0	1.0	1.0	2.0	...	1.0	2.0	1.0	0.0	1.0	2.0	1.0	2.0	1.0	1.0

4 rows × 66 columns

nprcols = [col for col in nprdf.columns if nprdf[col].quantile(0.985) > 1]
nprcols3 = [col for col in nprdf.columns if col not in nprcols and nprdf[col].max() == 1]
nprcols2 = [col for col in nprdf.columns if col not in nprcols and col not in nprcols3]
print(nprcols)
print(nprcols2)
print(nprcols3)

['Adcyap1r1', 'Cntfr', 'Ghr', 'Igf1r', 'Insr', 'Ntrk2', 'Ntsr2']
['Avpr1a', 'Calcrl', 'Esr1', 'Galr3', 'Ghsr', 'Glp1r', 'Gpr55', 'Gpr83', 'Gpr149', 'Hcrtr1', 'Insrr', 'Mc1r', 'Mc4r', 'Nmur1', 'Nmur2', 'Npr2', 'Npr3', 'Ntsr1', 'Oprm1', 'Prlr', 'Sstr1', 'Sstr3', 'Tacr3', 'Tshr']
['Calcr', 'Cckar', 'Cckbr', 'Crhr1', 'Crhr2', 'Galr1', 'Galr2', 'Grpr', 'Hcrtr2', 'Kiss1r', 'Lepr', 'Mc3r', 'Mchr1', 'Nmbr', 'Npffr1', 'Npffr2', 'Npr1', 'Npsr1', 'Npy1r', 'Npy2r', 'Npy5r', 'Oprd1', 'Oprk1', 'Oprl1', 'Oxtr', 'Prlhr', 'Prokr2', 'Qrfpr', 'Rxfp1', 'Rxfp2', 'Sstr2', 'Tacr1', 'Trhr', 'Vipr1', 'Vipr2']

sc.pl.umap(
    adata_eval2,
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nprcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-npr2" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr2-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nprcols3,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    na_color="#9A9FB080",
    save="-npr3" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr3-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

nmrdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in nmr if g in adata_eval2.var_names])]
nmrdf.describe()

	Adra1a	Adra1b	Adra2a	Adra2b	Adra2c	Adrb1	Adrb2	Adrb3	Adrm1	Adora1	...	P2rx7	P2ry1	P2ry2	P2ry6	P2ry12	P2ry13	P2ry14	Ryr1	Ryr2	Ryr3
count	3215.000000	3215.000000	3215.000000	3215.0	3215.000000	3215.000000	3215.000000	3215.0	3215.000000	3215.000000	...	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000
mean	0.062208	0.027061	0.066874	0.0	0.000622	0.036081	0.000622	0.0	0.049767	0.116019	...	0.044168	0.022084	0.000933	0.000311	0.008398	0.000311	0.128149	0.000622	0.042924	0.007776
std	0.273013	0.173408	0.266708	0.0	0.024938	0.191459	0.024938	0.0	0.223145	0.356173	...	0.212936	0.149081	0.030538	0.017636	0.091270	0.017636	0.367132	0.024938	0.234060	0.087852
min	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	3.000000	2.000000	4.000000	0.0	1.000000	2.000000	1.000000	0.0	2.000000	4.000000	...	2.000000	2.000000	1.000000	1.000000	1.000000	1.000000	3.000000	1.000000	3.000000	1.000000

8 rows × 135 columns

nmrcols = [col for col in nmrdf.columns if nmrdf[col].quantile(0.985) > 1]
nmrcols3 = [col for col in nmrdf.columns if col not in nmrcols and nmrdf[col].max() == 1]
nmrcols2 = [col for col in nmrdf.columns if col not in nmrcols and col not in nmrcols3]
print(nmrcols)
print(nmrcols2)
print(nmrcols3)

['Gria1', 'Gria2', 'Grid1', 'Grid2', 'Grik2', 'Grin2b', 'Grin2c', 'Grin3a', 'Grm5', 'Grm3', 'Gabrb1', 'Gabrg1', 'Gabrg3', 'Gabbr1', 'Gabbr2', 'Gnas', 'Gnai2', 'Gnao1', 'Gnaq', 'Gna13', 'Gnb1', 'Gnb2', 'Gng5', 'Gng12']
['Adra1a', 'Adra1b', 'Adra2a', 'Adra2b', 'Adrb1', 'Adrb3', 'Adrm1', 'Adora1', 'Adora2b', 'Chrm5', 'Chrna3', 'Chrna10', 'Chrnb1', 'Chrnb2', 'Chrnb3', 'Gria3', 'Gria4', 'Grik1', 'Grik3', 'Grik4', 'Grik5', 'Grin1', 'Grm1', 'Grm4', 'Grm7', 'Grm8', 'Gabra1', 'Gabra2', 'Gabra3', 'Gabra4', 'Gabra5', 'Gabrb2', 'Gabrb3', 'Gabrg2', 'Gabrq', 'Drd2', 'Drd4', 'Htr1b', 'Htr1d', 'Htr2c', 'Htr3b', 'Gnai1', 'Gnai3', 'Gna11', 'Gna12', 'Gnal', 'Gnb3', 'Gnb4', 'Gnb5', 'Gng2', 'Gng3', 'Gng7', 'Gng10', 'Gng11', 'Gng13', 'P2rx2', 'P2rx4', 'P2rx6', 'P2rx7', 'P2ry1', 'P2ry14', 'Ryr2']
['Adra2c', 'Adrb2', 'Adora2a', 'Adora3', 'Chrm1', 'Chrm2', 'Chrm3', 'Chrm4', 'Chrna4', 'Chrna5', 'Chrna6', 'Chrna7', 'Chrna9', 'Grin2a', 'Grin2d', 'Grin3b', 'Grm2', 'Grm6', 'Gabrd', 'Gabre', 'Gabrp', 'Gabrr1', 'Gabrr2', 'Gabrr3', 'Drd1', 'Drd3', 'Drd5', 'Htr1a', 'Htr1f', 'Htr2a', 'Htr2b', 'Htr3a', 'Htr4', 'Htr5a', 'Htr5b', 'Htr6', 'Htr7', 'Gng4', 'Gng8', 'Gngt2', 'P2rx1', 'P2rx3', 'P2rx5', 'P2ry2', 'P2ry6', 'P2ry12', 'P2ry13', 'Ryr1', 'Ryr3']

sc.pl.umap(
    adata_eval2,
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nmrcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-nmr2" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr2-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nmrcols3,
    frameon=False,
    show=True,
    size=40,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    vmin=0.0,
    na_color="#9A9FB080",
    save="-nmr3" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr3-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

adddf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in genes_embed if g in adata_eval2.var_names])]
adddf.describe()

	Abcd1	Abcd2	Abcd3	Acaa2	Acox1	Agt	Aldh1a1	Aldh1l1	Aldoc	Apoe	...	Slc4a4	Slc6a11	Slc7a10	Slc38a1	Slit1	Slit2	Sox9	Tafa1	Tkt	Trpv1
count	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	...	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.0
mean	0.018974	0.180093	0.237636	0.095179	0.161742	1.441058	0.358942	0.266874	2.058787	19.309486	...	3.691135	4.187870	1.088647	0.093002	0.005599	0.384137	0.484914	0.182893	0.231104	0.0
std	0.138714	0.448577	0.504012	0.311007	0.418867	1.831972	0.944605	0.539371	2.299718	15.224814	...	3.610910	3.795837	1.295694	0.506704	0.086232	0.896059	0.795331	0.648693	0.493672	0.0
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.500000	9.000000	...	1.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0
50%	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	1.000000	15.000000	...	3.000000	3.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0
75%	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	3.000000	25.000000	...	5.000000	6.000000	2.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.0
max	2.000000	6.000000	4.000000	3.000000	3.000000	16.000000	9.000000	4.000000	21.000000	128.000000	...	26.000000	38.000000	10.000000	8.000000	2.000000	10.000000	6.000000	9.000000	4.000000	0.0

8 rows × 78 columns

agcols = [col for col in adddf.columns if adddf[col].quantile(0.985) > 1]
agcols3 = [col for col in adddf.columns if col not in agcols and adddf[col].max() == 1]
agcols2 = [col for col in adddf.columns if col not in agcols and col not in agcols3]
print(agcols)
print(agcols2)
print(agcols3)

['Abcd2', 'Abcd3', 'Acox1', 'Agt', 'Aldh1a1', 'Aldh1l1', 'Aldoc', 'Apoe', 'Aqp4', 'Ckb', 'Cst3', 'Dcc', 'Ech1', 'Egfr', 'Eno1', 'Fgfr3', 'Fis1', 'Fos', 'Fth1', 'Ftl1', 'Gfap', 'Gja1', 'Glul', 'Hacd2', 'Hadhb', 'Hepacam', 'Htra1', 'Lxn', 'Mfn1', 'Mgll', 'Ndrg2', 'Nfia', 'Pla2g7', 'Plcb1', 'Pygb', 'S100a6', 'S100b', 'Scd2', 'Sgcd', 'Slc1a2', 'Slc1a3', 'Slc4a4', 'Slc6a11', 'Slc7a10', 'Slc38a1', 'Slit2', 'Sox9', 'Tafa1', 'Tkt']
['Abcd1', 'Acaa2', 'Cnr1', 'Cnr2', 'Dagla', 'Daglb', 'Decr2', 'Gli1', 'Lcat', 'Lpcat3', 'Mfn2', 'Mief1', 'Napepld', 'Opa1', 'Otp', 'Pex1', 'Pex12', 'Pex13', 'Pex14', 'Pex16', 'Pex2', 'Pex3', 'Pex6', 'Slit1', 'Trpv1']
['Dnm1', 'Faah', 'Pex10', 'Pex26']

sc.pl.umap(
    adata_eval2,
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=agcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-adgen2" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen2-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=agcols3,
    frameon=False,
    show=True,
    size=40,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    vmin=0.0,
    na_color="#9A9FB080",
    save="-adgen3" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen3-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

tfdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in tf_genes if g in adata_eval2.var_names])]
tfdf.describe()

	Aff3	Alx1	Arid1a	Arid1b	Arid2	Arid3a	Arid3b	Arid4a	Arid4b	Arid5b	...	Caskin1	Gtf3c4	Gcdh	BC024139	Zfp398	Tcfl5	A630089N07Rik	Phf8	Zhx3	Zfp82
count	3215.000000	3215.0	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	...	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000	3215.000000
mean	0.131882	0.0	0.173250	0.161120	0.134992	0.000933	0.010575	0.109176	0.183204	0.094557	...	0.113219	0.025194	0.040124	0.000622	0.072162	0.000311	0.068429	0.055677	0.312597	0.020529
std	0.389694	0.0	0.433686	0.419107	0.382982	0.030538	0.102308	0.363503	0.459665	0.327753	...	0.357508	0.160661	0.199427	0.024938	0.281817	0.017636	0.280537	0.238640	0.645418	0.148258
min	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	3.000000	0.0	4.000000	3.000000	3.000000	1.000000	1.000000	7.000000	3.000000	3.000000	...	3.000000	2.000000	2.000000	1.000000	3.000000	1.000000	3.000000	2.000000	7.000000	2.000000

8 rows × 1336 columns

tfdf.quantile(q=[0.5, 0.9, 0.985, 1.0], axis=0)

	Aff3	Alx1	Arid1a	Arid1b	Arid2	Arid3a	Arid3b	Arid4a	Arid4b	Arid5b	...	Caskin1	Gtf3c4	Gcdh	BC024139	Zfp398	Tcfl5	A630089N07Rik	Phf8	Zhx3	Zfp82
0.500	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.900	1.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
0.985	2.0	0.0	2.0	2.0	1.0	0.0	0.0	1.0	2.0	1.0	...	1.0	1.0	1.0	0.0	1.0	0.0	1.0	1.0	2.0	1.0
1.000	3.0	0.0	4.0	3.0	3.0	1.0	1.0	7.0	3.0	3.0	...	3.0	2.0	2.0	1.0	3.0	1.0	3.0	2.0	7.0	2.0

4 rows × 1336 columns

tfcols = [col for col in tfdf.columns if tfdf[col].quantile(0.9) > 3]
tfcols3 = [col for col in tfdf.columns if col not in tfcols and tfdf[col].max() == 1]
tfcols2 = [col for col in tfdf.columns if col not in tfcols and tfdf[col].max() > 1]

print(tfcols)
print(tfcols2)
print(tfcols3)

['Rora', 'Hes5', 'Npas3', 'Nfia', 'Trps1', 'Zbtb20', 'Ank2']
['Aff3', 'Arid1a', 'Arid1b', 'Arid2', 'Arid4a', 'Arid4b', 'Arid5b', 'Bcl6', 'Bdp1', 'Btaf1', 'Bag1', 'Bcor', 'Brf1', 'Bach2', 'Bmi1', 'Ctbp1', 'Ctbp2', 'C1d', 'Cebpb', 'Cebpd', 'Cebpg', 'Cebpz', 'Ctcf', 'Cnot3', 'Cops2', 'Crebbp', 'Cited2', 'Dbp', 'Dpf2', 'Ddx5', 'Ddx3x', 'Ddit3', 'Drap1', 'Ep300', 'Ets1', 'Ets2', 'E2f1', 'E2f3', 'E2f4', 'E2f5', 'E2f6', 'E4f1', 'Elf1', 'Elf2', 'Elk3', 'Elk4', 'Eaf2', 'Eid1', 'Erf', 'Ewsr1', 'Fosb', 'Fos', 'Fli1', 'Gabpa', 'Gatad2b', 'Gli1', 'Gli2', 'Gli3', 'Glis2', 'Herc1', 'Ikzf3', 'Isl1', 'Irx3', 'Kat2a', 'Kat2b', 'Kat5', 'Kat6a', 'Klf1', 'Klf10', 'Klf12', 'Klf13', 'Klf15', 'Klf4', 'Klf5', 'Klf7', 'Klf9', 'Kcnip3', 'Lpp', 'Lhx2', 'Mxd1', 'Mxi1', 'Mlx', 'Mecom', 'Mybbp1a', 'Mycbp', 'Maz', 'Max', 'Meis1', 'Meis2', 'Nkx2-1', 'Nkx6-2', 'Nab2', 'Paxip1', 'Pou2f1', 'Pou2f2', 'Pou3f2', 'Pou3f4', 'Pou6f1', 'Patz1', 'Pknox2', 'Pknox1', 'Rab11fip3', 'Rorb', 'Rest', 'Rybp', 'Rrn3', 'Arhgap35', 'Sertad2', 'Smyd1', 'Smyd3', 'Setdb1', 'Skil', 'Smad3', 'Smad5', 'Smad9', 'Snw1', 'Spen', 'Sox1', 'Sox10', 'Sox11', 'Sox13', 'Sox18', 'Sox2', 'Sox21', 'Sox4', 'Sox5', 'Sox6', 'Sox8', 'Sox9', 'Sub1', 'Smarcal1', 'Smarca1', 'Smarca2', 'Smarca4', 'Smarca5', 'Smarcb1', 'Smarcc1', 'Smarcd1', 'Smarcd2', 'Smarcd3', 'Smarce1', 'Smarcad1', 'Son', 'Tbr1', 'Tbpl1', 'Tmf1', 'Taf1', 'Taf10', 'Taf11', 'Taf12', 'Taf2', 'Taf3', 'Taf4b', 'Taf6l', 'Taf6', 'Taf8', 'Taf9', 'Taf1a', 'Taf1c', 'Tead1', 'Tead2', 'Tead3', 'Traf4', 'Tsc22d1', 'Snrpc', 'Snrpb2', 'Wdr5', 'Wwtr1', 'Wwox', 'Xbp1', 'Ybx1', 'Yy1', 'Ascl1', 'Ascc1', 'Atf2', 'Atf4', 'Atf5', 'Atf6', 'Atf7ip', 'Atf7', 'Ar', 'Aatf', 'Apex1', 'Arnt2', 'Arnt', 'Arntl', 'Atr', 'Atm', 'Atoh8', 'Relb', 'Bhlhe40', 'Bhlhe41', 'Blzf1', 'Bmp7', 'Bin1', 'Bptf', 'Brd4', 'Brd7', 'Abl1', 'Creb1', 'Creb3', 'Crem', 'Camta2', 'Cic', 'Ctnnb1', 'Cdc5l', 'Creg1', 'Clock', 'Cux1', 'Cux2', 'Dmtf1', 'Ccnt1', 'Ccnt2', 'Cdk7', 'Cdk8', 'Cdk9', 'Cdkn1a', 'Ddb1', 'Ddb2', 'Dcp1a', 'Dtx1', 'Ebf1', 'Egr1', 'Egr2', 'Egr3', 'Eed', 'Epas1', 'Ezh1', 'Ezh2', 'Esr1', 'Esrra', 'Esrrg', 'Etv1', 'Etv3', 'Etv4', 'Etv5', 'Foxc1', 'Foxd1', 'Foxf2', 'Foxj2', 'Foxk1', 'Foxo1', 'Foxo3', 'Foxp1', 'Foxp2', 'Foxp4', 'Foxq1', 'Fosl2', 'Fus', 'Gtf2h1', 'Gtf2h2', 'Gtf2h4', 'Gtf2ird1', 'Gtf2i', 'Gtf2a1l', 'Gtf2b', 'Gtf3a', 'Gtf3c1', 'Gcm1', 'Gmeb1', 'Gmeb2', 'Grhl1', 'Hes6', 'Hey1', 'Hsf1', 'Hsbp1', 'Hltf', 'Hlf', 'Hnrnpab', 'Hnrnpc', 'Hnrnpdl', 'Hnrnph2', 'Hnrnpk', 'Hnrnpl', 'Hnrnpll', 'Hnrnpu', 'Hmgb1', 'Hmgb2', 'Hmgn1', 'Hmgn3', 'Hdac1', 'Hdac10', 'Hdac2', 'Hdac3', 'Hdac4', 'Hdac5', 'Hdac7', 'Hdac8', 'Hdac9', 'Hcfc1', 'Hivep1', 'Hivep2', 'Hif1a', 'Id1', 'Id2', 'Id3', 'Id4', 'Itgb3bp', 'Irf1', 'Irf2bp1', 'Irf2', 'Irf3', 'Irf7', 'Irf9', 'Jade1', 'Junb', 'Jund', 'Jun', 'Lbx1', 'Lztfl1', 'Lcor', 'Lef1', 'Kdm5a', 'Kdm5b', 'Kdm5c', 'Kmt2a', 'Maml1', 'Maml3', 'Mnt', 'Med1', 'Med12', 'Med14', 'Med15', 'Med17', 'Med21', 'Med23', 'Med24', 'Med6', 'Mnat1', 'Mtf1', 'Mtf2', 'Mta1', 'Mta2', 'Mecp2', 'Mbd1', 'Mbd2', 'Mitf', 'Mapk14', 'Mapk3', 'Mapk7', 'Myef2', 'Myt1', 'Mllt11', 'Mllt3', 'Mllt6', 'Mef2a', 'Mef2d', 'Nrg1', 'Npas1', 'Npas2', 'Notch1', 'Notch2', 'Notch3', 'Nfic', 'Nfix', 'Nfat5', 'Nfatc2', 'Nfatc3', 'Nfkb1', 'Nfkbia', 'Nfkbiz', 'Nfe2l1', 'Nufip1', 'Ncor1', 'Ncor2', 'Ncoa1', 'Ncoa2', 'Ncoa3', 'Ncoa4', 'Ncoa5', 'Ncoa6', 'Ncoa7', 'Nrip1', 'Nr1d1', 'Nr1d2', 'Nr1h2', 'Nr2c1', 'Nr2c2', 'Nr2e1', 'Nr2f1', 'Nr2f2', 'Nr2f6', 'Nr3c1', 'Nr3c2', 'Nr4a1', 'Nr6a1', 'Nsd1', 'Nrf1', 'Nfx1', 'Nfya', 'Nfyb', 'Npm1', 'Olig1', 'Olig2', 'Otx2', 'Pax6', 'Prrx1', 'Pitx2', 'Ptch1', 'Per1', 'Per2', 'Per3', 'Ppara', 'Ppard', 'Pgs1', 'Plagl2', 'Parp1', 'Parp12', 'Pabpn1', 'Pcbp1', 'Pcbp2', 'Pcbp4', 'Pbrm1', 'Pqbp1', 'Polr2a', 'Polr2b', 'Polr2k', 'Pbx1', 'Pbx3', 'Phb2', 'Preb', 'Pml', 'Psmc5', 'Pin1', 'Pias1', 'Pias2', 'Pias3', 'Pias4', 'Pura', 'Purb', 'Phtf1', 'Rreb1', 'Rbpj', 'Rfx2', 'Rfx4', 'Rfx5', 'Rfxank', 'Rfxap', 'Rel', 'Rax', 'Rb1', 'Rbbp4', 'Rbbp7', 'Rarb', 'Rxra', 'Rpl7', 'Rpl7a', 'Ring1', 'Rnf14', 'Rnf2', 'Rnf25', 'Rnf4', 'Runx3', 'Runx1t1', 'Sall1', 'Sall2', 'Sall3', 'Safb', 'Safb2', 'Scmh1', 'Stat1', 'Stat3', 'Stat5b', 'Six3', 'Sirt3', 'Ski', 'Satb1', 'Satb2', 'Syne1', 'Sra1', 'Srebf2', 'Srebf1', 'Strn', 'Ssrp1', 'Supt16', 'Supt4a', 'Supt5', 'Suv39h2', 'Suz12', 'Tdg', 'Tox', 'Thra', 'Thrap3', 'Thrb', 'Trip11', 'Tef', 'Topors', 'Sp1', 'Sp3', 'Sp4', 'Tcea2', 'Tcf12', 'Tcf20', 'Tcf3', 'Tcf4', 'Tcf7l1', 'Tcf7l2', 'Tfam', 'Tfcp2', 'Tfdp1', 'Tfdp2', 'Ttf1', 'Tada3', 'Sin3a', 'Sin3b', 'Tbl1x', 'Tle1', 'Tle2', 'Tle3', 'Tle4', 'Trp53', 'Trp63', 'Trrap', 'Mdm2', 'Mdm4', 'Tsn', 'Tgs1', 'Trim24', 'Trim25', 'Trim28', 'Trim30a', 'Trim33', 'Ubp1', 'Ubtf', 'Usf1', 'Usf2', 'Mafg', 'Rela', 'Vps72', 'Vezf1', 'Vhl', 'Zrsr1', 'Zc3h8', 'Zeb1', 'Zeb2', 'Zbtb16', 'Zscan21', 'Zfp106', 'Zfp110', 'Zfp128', 'Zfp148', 'Zfp239', 'Zfp263', 'Zfp326', 'Zfp422', 'Zfp423', 'Zfp467', 'Zfp503', 'Zfp60', 'Zfp64', 'Zfp668', 'Zfp68', 'Zfp90', 'Zic1', 'Zic2', 'Zic3', 'Zfpm2', 'Zkscan17', 'Zmym2', 'Zhx1', 'Zhx2', 'Adnp', 'Aebp1', 'Aebp2', 'Aip', 'Ank1', 'Ankfy1', 'Atf1', 'Zfp36l1', 'Cbfb', 'Cbx3', 'Cbx4', 'Ccnk', 'Cdk2', 'Cdk4', 'Cdkn2c', 'Cdkn2d', 'Chgb', 'Dach1', 'Dnmt1', 'Dnmt3a', 'Dr1', 'Drg1', 'Ell', 'Etv6', 'Bcl11a', 'Fah', 'Fem1a', 'Fem1b', 'Foxn2', 'Gas7', 'Mtpn', 'Baz1a', 'Hes1', 'Hey2', 'Foxj1', 'Hipk2', 'Hira', 'Hmgn2', 'Hmg20b', 'Hmgb3', 'Hsf2', 'Ifnar2', 'Ikbkb', 'Ikbkg', 'Ilf3', 'Invs', 'Irx1', 'Irx2', 'Klf2', 'Klf3', 'Hivep3', 'Ldb1', 'Ldb2', 'Lmo2', 'Lmo4', 'Maf', 'Mbd3', 'Mcm4', 'Mcm5', 'Mcm7', 'Mef2c', 'Rab8a', 'Men1', 'Mid1', 'Mllt10', 'Nab1', 'Naca', 'Ndn', 'Nedd8', 'Nfe2l2', 'Nfib', 'Nfkbib', 'Nfkbil1', 'Nfyc', 'Nkx2-2', 'Notch4', 'Sqstm1', 'Otp', 'Otx1', 'Pbx2', 'Pdcd11', 'Enpp2', 'Peg3', 'Phf2', 'Papola', 'Cnot7', 'Pou3f3', 'Ppargc1a', 'Scand1', 'Ppp1r1b', 'Ppp3cb', 'Ppp5c', 'Prkar1a', 'Prox1', 'Psmc3', 'Ptma', 'Rab11b', 'Rab18', 'Rai1', 'Rbl2', 'Rfx3', 'Rnps1', 'Rorc', 'Polr2c', 'Polr2j', 'Khdrbs1', 'Sap18', 'Ostf1', 'Snrpb', 'Sox12', 'Zfp143', 'Stag1', 'Stat2', 'Strap', 'Taf1b', 'Tbp', 'Tcea1', 'Thrsp', 'Tial1', 'Tmpo', 'Ppp1r13b', 'Trip6', 'Tob1', 'Trp73', 'Tsg101', 'Baz1b', 'Zfp1', 'Zfp13', 'Zfp2', 'Zfp207', 'Zfp27', 'Zfp36', 'Zfp37', 'Zfp40', 'Zfp41', 'Zfp52', 'Zfp9', 'Zfp94', 'Zfx', 'Zfy2', 'Ash2l', 'Bpnt1', 'Dido1', 'Dazap2', 'Ing1', 'Map3k12', 'Mapk1', 'Mapk8', 'Zfp146', 'Cops5', 'Polr3e', 'Sh3d19', 'Trp53bp1', 'Ing4', 'Limd1', 'Mga', 'Zfp292', 'Pdlim4', 'Zfp354c', 'Abt1', 'Fbxw7', 'Nrbf2', 'Lsm4', 'Keap1', 'Ppp2r1a', 'Ccnc', 'Ppp1r10', 'Carhsp1', 'Plrg1', 'Pla2g6', 'Psmd10', 'Hif3a', 'Rab11a', 'Ddx20', 'Deaf1', 'Irx5', 'Tcerg1', 'Heyl', 'Zfp386', 'Rbm14', 'Zfp113', 'Trip4', 'Ruvbl1', 'Rps6ka4', 'Usp49', 'Zfp112', 'Jmy', 'Carm1', 'Sap30', 'Foxb1', 'Sirt2', 'Nmi', 'Asb3', 'Znrd1', 'Dmap1', 'Polr2e', 'Polr2l', 'Zmynd11', 'Mcm8', 'Ctnnbl1', 'Ccnh', 'Hmg20a', 'Ssbp2', 'Zdhhc6', 'Yaf2', 'Polr3d', 'Nrarp', 'Rnf141', 'Psmd9', 'Pfdn1', 'Zfp606', 'Dedd2', 'Xab2', 'Rabl3', 'Polr2g', 'Sec14l2', 'Sncaip', 'Gtf2e2', 'Zdhhc21', 'Sirt5', 'Phf5a', 'Ankra2', 'Gtf2f2', 'Tulp4', 'Zfp580', 'Ppp1r12b', 'Zfp219', 'Polr2i', 'Dpf3', 'Basp1', 'Polr3b', 'Arhgap17', 'Bbx', 'Zcchc8', 'Prdm16', 'Prdm5', 'Zdhhc1', 'Zfp597', 'Fmnl2', 'Pogk', 'Ing3', 'Phf7', 'Ddx54', 'Phf10', 'Asb6', 'Zfp131', 'Ssbp3', 'Bclaf1', 'Zfp248', 'Wasl', 'Nptxr', 'Hbp1', 'Rnf38', 'Btbd11', 'Zdhhc16', 'Gtf2e1', 'Cxxc1', 'Zfp84', 'Prr3', 'Sirt4', '1700003F12Rik', 'Acyp2', 'Rai14', 'Snip1', 'Ssbp4', 'Zfp142', 'Lbh', '4930522L14Rik', 'Helz', 'Asb8', 'Brd8', 'Sp2', 'Polr3h', 'Mynn', 'Sp6', 'Gtf2a1', 'Sirt1', 'Maged1', 'Ptges2', 'Zfp451', 'Taf13', 'Zfp282', 'Taf5l', 'Snapc2', 'Ankrd10', 'Rcor1', 'Tcf19', 'Bmyc', 'Chd4', 'Ankhd1', 'Uhrf2', 'Lmo1', 'Zfp91', 'Dek', 'Dbx2', 'Brdt', 'Pawr', 'Baz2a', 'Mta3', 'Ivns1abp', 'Ankrd6', 'Ubn1', 'Zfp287', 'Zfp617', 'Ash1l', 'Ell2', 'Dot1l', 'Hmgcs1', 'Sirt7', '6720489N17Rik', 'Zswim4', 'Myocd', 'L3mbtl2', 'Polrmt', 'Ugp2', 'Btf3', 'Zfp523', 'Trerf1', 'Zfp281', 'Pms1', 'Zdhhc5', 'Ebf4', 'Nkx2-4', 'Asxl1', 'Ppp1r16b', 'Pbxip1', 'Ddx58', 'Glis1', 'Tardbp', 'Creb5', 'Ppp1r12c', 'Zfp592', 'Zfp553', 'Zfp612', 'Zfp426', 'Rab8b', 'Scap', 'Tceal1', 'BC024063', 'Zfp454', 'Fem1c', 'Setbp1', 'Carf', 'Dmrta1', 'Polr2h', 'Zfp277', 'Shprh', 'Phf12', 'Zbtb1', 'Caskin1', 'Gtf3c4', 'Gcdh', 'Zfp398', 'A630089N07Rik', 'Phf8', 'Zhx3', 'Zfp82']
['Arid3a', 'Arid3b', 'Bcl3', 'Bach1', 'Barx2', 'Cebpa', 'Cited1', 'Cited4', 'E2f2', 'E2f7', 'Elf4', 'Elf5', 'Elk1', 'Eid2', 'Daxx', 'Fezf2', 'Gata1', 'Gata3', 'Gata4', 'Gsx1', 'Ikzf1', 'Irx6', 'Jdp2', 'Klf16', 'Lmx1b', 'Mdfi', 'Nkx6-1', 'Nanog', 'Pou3f1', 'Prdm1', 'Rbak', 'Rcor2', 'Spdef', 'Sertad1', 'Smad7', 'Sox15', 'Sox17', 'Sox3', 'Sox7', 'Sp7', 'Tbx1', 'Tbx2', 'Tbx21', 'Tbx3', 'Tbx6', 'Taf7l', 'Taf7', 'Tgif1', 'Txk', 'Atf3', 'Arx', 'Ahr', 'Atoh7', 'Batf', 'T', 'Creb3l1', 'Creb3l4', 'Cdc6', 'Ciita', 'Crxos', 'Cbfa2t3', 'Dlx2', 'Dlx3', 'Egr4', 'Emx1', 'Emx2', 'En1', 'Esr2', 'Esrrb', 'Esx1', 'Foxd2', 'Foxg1', 'Foxo4', 'Foxo6', 'Foxp3', 'Fosl1', 'Fhl2', 'Gbx2', 'Grhl3', 'Gadd45a', 'Hes7', 'Hsf4', 'Hmga1', 'Hdac6', 'Hoxa1', 'Hesx1', 'Hic1', 'Irf8', 'Mn1', 'Meox1', 'Mesp2', 'Msx1', 'Msx2', 'Msc', 'Myt1l', 'Myb', 'Mybl1', 'Mybl2', 'Myc', 'Mzf1', 'Mllt1', 'Mef2b', 'Nrl', 'Neurod2', 'Npas4', 'Nfatc1', 'Nfatc4', 'Nfkb2', 'Nfkbie', 'Nfe2l3', 'Nfil3', 'Nrip3', 'Nr1h3', 'Nr1h5', 'Nr4a2', 'Nr4a3', 'Nr5a1', 'Nr5a2', 'Onecut1', 'Onecut2', 'Onecut3', 'Pax1', 'Pax2', 'Pax3', 'Pax5', 'Pax9', 'Prrx2', 'Prrxl1', 'Pitx1', 'Ppargc1b', 'Pparg', 'Plag1', 'Plagl1', 'Pcgf2', 'Pcgf6', 'Pbx4', 'Pgr', 'Pdcd7', 'Pcbd1', 'Rbpjl', 'Rsl1', 'Rfx1', 'Rhox8', 'Rbl1', 'Rara', 'Rarg', 'Rxrb', 'Runx1', 'Runx2', 'Scrt1', 'Srf', 'Scml2', 'Stat5a', 'Stat6', 'Six4', 'Sim2', 'Snapc4', 'Snai1', 'Snai2', 'Snai3', 'Spi1', 'Suv39h1', 'Tcf7', 'Tfap4', 'Tfe3', 'Tfec', 'Tle6', 'Tgfb1i1', 'Tsc2', 'Twist2', 'Uaca', 'Mafa', 'Mafb', 'Maff', 'Mafk', 'Mycn', 'Vav1', 'Vax1', 'Vsx1', 'Vsx2', 'Vdr', 'Zbtb14', 'Zbtb17', 'Zbtb33', 'Zbtb7b', 'Zfy1', 'Zfp354a', 'Zfp472', 'Zik1', 'Zic4', 'Zic5', 'Zfpm1', 'Zmynd10', 'Ahrr', 'Neurog2', 'Bard1', 'Bcl6b', 'Brca1', 'Camk4', 'Cbx2', 'Cdkn2b', 'Dbx1', 'Dlx1', 'Ebf2', 'Ebf3', 'Ercc2', 'Ercc3', 'Erg', 'Gabpb1', 'Hcls1', 'Hlx', 'Hmga2', 'Foxa3', 'Htr5a', 'Elavl2', 'Irf4', 'Lyl1', 'Ascl2', 'Mcm3', 'Mcm2', 'Mcm6', 'Mpl', 'Nfatc2ip', 'Uhrf1', 'Musk', 'Pou2f3', 'Ruvbl2', 'Rxrg', 'Shox2', 'Siah2', 'Sim1', 'Six5', 'Six6', 'Ighmbp2', 'Tbx15', 'Tcea3', 'Phf1', 'Tnfaip3', 'Dedd', 'Zfp105', 'Zfp35', 'Zfp39', 'Zfp46', 'Zfp51', 'Zfp54', 'Zfp57', 'Zfp59', 'Zfp61', 'Zfp92', 'Zfp93', 'Zfp97', 'Zim1', 'Zfp275', 'Zfp354b', 'Hnf4g', 'Cbx8', 'Sirt6', 'Dmrt1', 'Htatip2', 'Insm1', 'Pdlim1', 'Lrrc6', 'Zfp108', 'Nupr1', 'Zfp235', 'Zfp111', 'Zfp109', 'Fhl5', 'Hic2', 'Shank3', 'Nrip2', 'Sp5', 'Asb1', 'Asb4', 'Asb2', 'Fank1', '4930430A15Rik', '1700020N01Rik', 'Nudt12', 'Cnot8', 'Trip13', '2810021J22Rik', 'Asb12', '4931423N10Rik', 'Fbxo24', '4933429H19Rik', 'Lsm11', 'Nkd2', 'Xrcc3', 'Hsf2bp', 'Zbtb3', 'Asb5', 'Brpf1', 'Asb15', '9130019O22Rik', 'Zfp319', 'Abtb1', 'Zfp202', 'Dach2', 'Rab15', 'Zdhhc15', 'Lmx1a', 'Foxn4', 'Rem2', 'Clp1', 'Zfp180', 'Sox30', 'Gls2', 'Taf5', 'Tgif2', 'Zfp334', 'Zfp189', 'Phf13', 'Gbx1', 'Hdac11', 'Homez', 'St18', 'Dmrta2', 'Bsx', 'Obox3', 'BC024139', 'Tcfl5']

for gene in tfcols:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        vmax="p90.0",
        na_color="#9A9FB080",
        save="-tf1-" + gene + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf1-Rora-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

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for gene in tfcols2:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        na_color="#9A9FB080",
        save="-tf2" + gene + save_PRJNA547712,
)

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for gene in tfcols3:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
        vmin=0.0,
        na_color="#9A9FB080",
        save="-tf3" + gene + save_PRJNA547712,
)

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sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-npr-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-nmr-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-adgen-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=tfcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-tf" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-tf-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-npr-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-nmr-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-adgen-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=tfcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-tf" + save_PRJNA547712,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-tf-best_xgboost-subregional_supervised-PRJNA547712-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

Zeisel et al. 2018

PRJNA438862_counts = sc.read_h5ad(
    f"data/PRJNA438862/data/PRJNA438862-astrocytes_dataset-{cb_fpr}-scenic_plus.h5ad"
)

PRJNA438862_regulons = sc.read_h5ad(
    f"data/PRJNA438862/data/PRJNA438862-astrocytes_dataset-{cb_fpr}-regulons.h5ad"
)

adata_eval2 = PRJNA438862_counts.copy()
# sc.pp.filter_genes(adata_eval2, min_cells=5) # filter genes should be matched with training set
sc.pp.normalize_total(adata_eval2, target_sum=1e4)
sc.pp.log1p(adata_eval2)
sc.pp.regress_out(adata_eval2, ["nCount_RNA", "percent_mito_ribo"])

normalizing counts per cell

    finished (0:00:00)

regressing out ['nCount_RNA', 'percent_mito_ribo']

    sparse input is densified and may lead to high memory use

    finished (0:00:54)

df_eval2 = pd.DataFrame(
    adata_eval2.X, columns=adata_eval2.var_names, index=adata_eval2.obs_names
)

df_eval2 = df_eval2.loc[
    :, [g for g in uni_fs if g in adata_eval2.var_names]
]
df_eval2.head()

	0610005C13Rik	Lpl	Lpp	Lrat	Lrba	Lrch3	0610043K17Rik	Lrguk	Lrig1	1110008P14Rik	...	Lncpint	Lnpep	Lonrf2	Lor	Loxhd1	Loxl2	Lpar1	Lpar3	Lpgat1	Lpin2
SRR6854065_AAAGCAGACGTAAC-1	-0.002658	-0.000961	-0.375886	-0.003982	-0.114099	-0.078103	-0.006533	-0.006947	-0.416043	-0.055583	...	-0.084890	-0.088658	-0.039388	-0.009645	-0.002052	-0.000653	-0.041048	0.000135	-0.044094	-0.149520
SRR6854065_ACAAATTGCTATTC-1	-0.004175	0.000463	5.178380	-0.002946	-0.151464	-0.113754	-0.008432	-0.005711	-0.554577	-0.034671	...	-0.113257	-0.107372	-0.035086	-0.006840	-0.001445	-0.000913	-0.024840	-0.000474	-0.027725	-0.134184
SRR6854065_ACATTCTGTACGAC-1	-0.005991	0.000070	-0.560662	-0.002325	-0.209604	-0.175139	-0.010695	-0.006247	-0.781745	-0.024186	...	-0.142716	-0.145771	-0.027693	-0.003660	-0.001387	-0.001071	-0.015187	-0.000929	-0.014565	-0.147209
SRR6854065_ACCCAGCTTGACTG-1	-0.005282	0.001430	-0.502479	-0.002211	-0.179185	-0.140406	-0.009818	-0.004878	-0.657752	-0.019910	...	-0.133801	-0.121573	-0.031871	-0.004800	-0.001025	-0.001097	-0.013348	-0.000909	-0.016001	-0.124067
SRR6854065_ACTAAAACGACGGA-1	-0.002886	-0.001448	2.389668	-0.004034	-0.124200	-0.089718	-0.006814	-0.007435	-0.457378	-0.057304	...	-0.087650	-0.096821	-0.037991	-0.009283	-0.002184	-0.000641	-0.041872	0.000135	-0.043785	-0.157708

5 rows × 4168 columns

X_uni_to_PRJNA438862 = X_uni.loc[:, [g for g in uni_fs if g in adata_eval2.var_names]]
subregional_model.fit(X_uni_to_PRJNA438862, codes)

joblib.dump(
    subregional_model,
    f"models/svm/best-logit-subregional_PRJNA438862_astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle",
)

print(X_uni_to_PRJNA438862.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist())

['0610005C13Rik', '0610043K17Rik', '1190005I06Rik', 'Lrp8os2', '1500004A13Rik', 'Lrrc18', '1500015L24Rik', '1600010M07Rik', '1600012H06Rik', 'Lrrc45', 'Lrrc57', '1700003D09Rik', '1700003G18Rik', '1700003M07Rik', '1700008C04Rik', 'Lrrd1', 'Lrrk1', 'Lsm8', '1700022H01Rik', '1700024B05Rik', 'Ltbp1', '1700025F22Rik', '1700027I24Rik', '1700028E10Rik', '1700029I15Rik', '1700030F04Rik', 'Ly86', '1700030L22Rik', '1700030N03Rik', '1700034I23Rik', 'Lysmd4', 'M1ap', '1700054A03Rik', '1700060O08Rik', '1700061G19Rik', '1700064H15Rik', 'Mageb18', '1700081N11Rik', '1700084C06Rik', '1700095A13Rik', '1700095J03Rik', 'Mamdc2', 'Mamdc4', 'Maml3', '1700102P08Rik', '1700108F19Rik', 'Mannr', 'Maob', 'Map1b', '1700125G22Rik', 'Map2k4', 'Map3k11', '1810020O05Rik', '1810030O07Rik', 'Map4k3', 'Map7', '2010007H06Rik', '2210016F16Rik', '2310030A07Rik', '2310030G06Rik', '2310039L15Rik', 'Masp1', '2310068J16Rik', '2510009E07Rik', '2610001J05Rik', '2600006K01Rik', 'Mboat4', '2610307P16Rik', 'Mccc1os', '2610318N02Rik', '2700033N17Rik', '2700097O09Rik', 'Mcm6', '2810030D12Rik', '2810410L24Rik', 'Mcur1', 'Mdfi', '2810459M11Rik', '2900052N01Rik', 'Mdp1', '3110021N24Rik', '3110039M20Rik', '3110056K07Rik', '3200001D21Rik', '3222401L13Rik', 'Mef2c', 'Meg3', '4731419I09Rik', 'Meikin', 'Meis2', '4732496C06Rik', '4833422C13Rik', 'Metap1d', '4833423E24Rik', 'Mettl11b', 'Mettl17', '4921507P07Rik', '4921511C10Rik', 'Mettl24', '4921539H07Rik', '4930404I05Rik', '4930404N11Rik', 'Mfng', 'Mfsd14b', 'Mfsd2a', 'Mfsd3', '4930415C11Rik', '4930415O20Rik', 'Mfsd7a', 'Mgam', 'Mgam2-ps', '4930432B10Rik', '4930439D14Rik', '4930441H08Rik', '4930445B16Rik', '4930447C04Rik', 'Mid1', 'Miip', 'Mill2', '4930467E23Rik', '4930470G03Rik', 'Mir124-2hg', '4930473A02Rik', 'Mlh3', '4930512B01Rik', 'Mmp11', 'Mmrn2', '4930529K09Rik', 'Mogat1', '4930545L23Rik', 'Mok', '4930549C15Rik', 'Morn2', '4930554G24Rik', '4930554H23Rik', '4930555B11Rik', 'Morn5', '4930556G01Rik', '4930556J24Rik', 'Mpg', 'Mpped1', 'Mpped2', '4930589L23Rik', 'Mrgpra6', '4930594M22Rik.1', '4931403E22Rik', 'Mroh7', '4931413K12Rik', '4932411K12Rik', '4932416K20Rik', '4932435O22Rik', '4932443L11Rik', 'Mrpl42', '4933403O08Rik', '4933406F09Rik', 'Mrps18b', '4933408J17Rik', '4933421A08Rik', 'Msantd2', 'Msh6', '4933431E20Rik', 'Msl3', '4933433H22Rik', 'Mta1', 'Mta3', '5031434O11Rik', 'Mtcp1', '5033426O07Rik', 'Mtfp1', 'Mtg1', '5430416N02Rik', 'Mthfs', '5430431A17Rik', 'Mtmr4', '5730522E02Rik', '5830444B04Rik', 'Mttp', 'Mtus1', '5930435M05Rik', '6030498E09Rik', '6330403K07Rik', 'Mus81', 'Mvd', '6430503K07Rik', '6330576A10Rik', '6430553K19Rik', '6430628N08Rik', '6530402F18Rik', '6530409C15Rik', '6530411M01Rik', '6720427I07Rik', 'Mycbp', 'Mycl', '8030453O22Rik', 'Myh6', 'Mylip', '9130008F23Rik', '9130017K11Rik', '9130221H12Rik', 'Myo1f', 'Myoc', 'Myorg', '9330158H04Rik', 'Myrfl', 'Myrip', '9330199G10Rik', 'Naa10', '9630001P10Rik', '9630014M24Rik', 'Naip1', '9630028H03Rik', 'Nalcn', 'Napb', 'A1bg', 'A230020J21Rik', 'A230051N06Rik', 'Nat8f7', 'Naxe', 'A230108P19Rik', 'A330076C08Rik', 'A330087D11Rik', 'A330094K24Rik', 'A4galt', 'Ncoa6', 'Ncor2', 'A530053G22Rik', 'Ndnf', 'A630001O12Rik', 'A630012P03Rik', 'A630023A22Rik', 'A730020M07Rik', 'A730049H05Rik', 'Ndufaf4', 'A730063M14Rik', 'A830035A12Rik', 'Necab3', 'Necap1', 'A930002C04Rik', 'Nectin1', 'A930006K02Rik', 'Nefh', 'Nek11', 'Nek8', 'AA465934', 'AB124611', 'Nell2', 'AC163638.2', 'AI429214', 'AI463229', 'Nfic', 'AI849053', 'Nfkbia', 'Nfkbib', 'Nfkbil1', 'AU023762', 'AW146154', 'Nhs', 'AY036118', 'Nifk', 'Aarsd1', 'Aasdhppt', 'Aass', 'Aatk', 'Nkain2', 'Nkd1', 'Nkx2-1', 'Nkx2-2', 'Nkx2-2os', 'Nkx2-4', 'Abcb1a', 'Abcb9', 'Nlgn2', 'Abcc10', 'Abcc2', 'Abcf2', 'Nme3', 'Abhd15', 'Nmnat2', 'Nmur1', 'Abl1', 'Ablim2', 'Abt1', 'Acap3', 'Notum', 'Acat2', 'Acat3', 'Ackr3', 'Ackr4', 'Acp1', 'Acsbg2', 'Nr1d1', 'Nr1d2', 'Nr2f2', 'Actr3b', 'Acvr1c', 'Acy3', 'Adam11', 'Nrg2', 'Adam5', 'Adamts12', 'Nsd2', 'Nsdhl', 'Nsf', 'Adamts2', 'Nsmce1', 'Nsmce3', 'Adat2', 'Ntn1', 'Ntn3', 'Ntng1', 'Nuak2', 'Adgrb2', 'Adgrf4', 'Adgrg2', 'Adgrl2', 'Nudt16l1', 'Adra1b', 'Adrb1', 'Nupr1l', 'Aff3', 'Afm', 'Agap2', 'Agbl1', 'Nxpe5', 'Oacyl', 'Ahctf1', 'Ahdc1', 'Ahsa1', 'Ogdhl', 'Ogfod2', 'Ogfod3', 'Ajm1', 'Olfml2b', 'Olfr1511', 'Akr1b3', 'Akt2', 'Olfr655', 'Olfr66', 'Olig2', 'Omg', 'Onecut2', 'Opa1', 'Alkal2', 'Alkbh7', 'Alpl', 'Alyref2', 'Amdhd2', 'Amer2', 'Ammecr1', 'Amot', 'Otoa', 'Amy1', 'Angel1', 'Otx1', 'Otx2', 'Otx2os1', 'Ovca2', 'Angptl4', 'Ankfn1', 'P2rx3', 'P2ry12', 'P2ry13', 'P2ry14', 'P2ry6', 'Ankrd22', 'P3h4', 'Pacsin1', 'Pacsin2', 'Padi2', 'Paf1', 'Anks1b', 'Ankzf1', 'Anln', 'Pank1', 'Paox', 'Anxa9', 'Aox1', 'Pard3bos1', 'Ap5s1', 'Apc2', 'Pawr', 'Pax5', 'Apln', 'Apmap', 'Apobec4', 'Aqp5', 'Pcdhb2', 'Aqp9', 'Ar', 'Pcdhb22', 'Pcgf1', 'Pck2', 'Arg2', 'Arhgap23', 'Pcsk2', 'Pcsk2os1', 'Arhgap31', 'Arhgap33', 'Arhgap40', 'Arhgap6', 'Pdcd1lg2', 'Arhgef10', 'Pde10a', 'Arhgef18', 'Arhgef28', 'Arhgef9', 'Arid3a', 'Pdgfb', 'Arl13a', 'Arl4d', 'Pdlim1', 'Armc2', 'Armcx4', 'Arntl', 'Pdzph1', 'Arpp21', 'Peak1os', 'Pebp4', 'Peg3', 'Arsj', 'Per2', 'Arx', 'Arvcf', 'Asb2', 'Pfkfb4', 'Ascl4', 'Asic1', 'Asic4', 'Asna1', 'Pglyrp1', 'Phactr2', 'Phf21b', 'Atic', 'Phldb2', 'Atoh7', 'Pi15', 'Atp23', 'Atp6v1e2', 'Atxn2l', 'Pitpnm3', 'Pitpnm2os2', 'Atxn7l2', 'Pitx2', 'Atxn7l3', 'Piwil4', 'Pkd1l2', 'Pkdcc', 'Pkhd1', 'Pkib', 'B130024G19Rik', 'Pla2g10', 'Pla2r1', 'Plat', 'Platr27', 'Platr8', 'Plbd1', 'B430010I23Rik', 'Plcb3', 'Pld5', 'B830042I05Rik', 'Plek2', 'Plekha6', 'BC017158', 'BC034090', 'Plekho2', 'BC037032', 'Plin3', 'Plk2', 'BC050972', 'BC051226', 'Plppr5', 'Bach2os', 'Plut', 'Bag3', 'Bahcc1', 'Baiap2', 'Baiap3', 'Bambi', 'Plxnc1', 'Pmepa1', 'Pmepa1os', 'Bbox1', 'Bbs1', 'Bcam', 'Bcar3', 'Pnpla2', 'Pnrc1', 'Bcas3os1', 'Podnl1', 'Pogk', 'Poglut1', 'Poglut2', 'Pold2', 'Bcl2l2', 'Polr1c', 'Becn1', 'Begain', 'Bet1l', 'Bicd2', 'Pou3f4', 'Bloc1s2', 'Bmp5', 'Bmp7', 'Bmx', 'Bnip2', 'Boc', 'Ppip5k2', 'Ppp1r12c', 'Ppp1r13b', 'Brd1', 'Ppp1r15a', 'Ppp1r16b', 'Brd3os', 'Ppp1r1b', 'Ppp1r26', 'Brip1os', 'Ppp1r3a', 'Ppp1r3d', 'Ppp1r9b', 'Ppp2r2c', 'Bsx', 'Btbd11', 'Btbd3', 'Btg2', 'Bves', 'Prag1', 'Prcd', 'C030007H22Rik', 'Prdm1', 'C030034L19Rik', 'Prdm8', 'Prdx4', 'Prelp', 'C1galt1c1', 'Prickle1', 'Prkaa2', 'C1s1', 'C2', 'Prkcb', 'Prkcd', 'C230034O21Rik', 'Prkcz', 'C2cd4c', 'C2cd4d', 'C330002G04Rik', 'C330013E15Rik', 'C4b', 'C530025M09Rik', 'C5ar1', 'C5ar2', 'Prox2', 'C8g', 'Prpf31', 'Cab39l', 'Prr11', 'Prrc2b', 'Cacna1h', 'Cacna2d2', 'Prrt1', 'Prrt2', 'Prss27', 'Prss36', 'Prss41', 'Prss45', 'Caln1', 'Calml4', 'Camk2a', 'Camk2b', 'Psme2b', 'Capsl', 'Ptch2', 'Ptchd4', 'Car15', 'Car3', 'Car8', 'Ptgs1', 'Pth1r', 'Carmil2', 'Casp6', 'Ptpn7', 'Catsperb', 'Ptprh', 'Catspere1', 'Ptpro', 'Cbfa2t3', 'Purg', 'Pygm', 'Ccdc134', 'Ccdc152', 'Ccdc162', 'Rab11fip4', 'Ccdc171', 'Rab20', 'Rab26os', 'Ccdc22', 'Ccdc24', 'Rab33b', 'Ccdc34os', 'Ccdc38', 'Ccdc84', 'Rabggtb', 'Ccdc85c', 'Ccdc86', 'Rad21l', 'Ccn1', 'Ralgapb', 'Ccn6', 'Ralyl', 'Rap1gap', 'Rapgef5', 'Cd164l2', 'Rasgrf1', 'Rasl11a', 'Rasl11b', 'Rassf2', 'Raver2', 'Rax', 'Rbis', 'Cd82', 'Rbm42', 'Cd96', 'Rbms2', 'Rbms3', 'Rbmxl1', 'Cdc42ep1', 'Rbpms', 'Cdc42ep4', 'Rbsn', 'Rcor2', 'Rcsd1', 'Cdh23', 'Recql5os1', 'Cdk10', 'Ceacam1', 'Rfxap', 'Cebpb', 'Rgl2', 'Rgs2', 'Cenpt', 'Rhcg', 'Rhd', 'Rhobtb1', 'Rhobtb2', 'Ribc2', 'Rif1', 'Rimbp2', 'Ces5a', 'Ripor2', 'Cfap100', 'Cfap161', 'Rmi1', 'Rmnd5a', 'Cfap46', 'Cfap52', 'Rnd1', 'Cfap69', 'Rnf121', 'Cfap77', 'Rnf122', 'Cfb', 'Cfp', 'Rnf152', 'Chd5', 'Rnf24', 'Rnf5', 'Chil1', 'Robo2', 'Rogdi', 'Ror1', 'Ror2', 'Chrac1', 'Chrdl1', 'Rpe65', 'Chst3', 'Ciart', 'Cib1', 'Cic', 'Rrp15', 'Rsf1os1', 'Rsf1os2', 'Rslcan18', 'Cklf', 'Ckmt2', 'Rspo2', 'Rtl4', 'Clcn6', 'Rubie', 'Rufy2', 'Rundc3a', 'Clec3b', 'Clic3', 'Ryr1', 'Clic6', 'S100pbp', 'S1pr5', 'Sall3', 'Samd12', 'Sap30bpos', 'Sap30l', 'Cmtr2', 'Pigm', 'Cndp2', 'Cnnm3', 'Scg5', 'Cntn2', 'Cntnap1', 'Scly', 'Scpep1os', 'Scrib', 'Col1a1', 'Col1a2', 'Scrt1', 'Scube1', 'Scrt2', 'Col25a1', 'Col4a3', 'Sdk2', 'Sds', 'Col9a2', 'Colec12', 'Sec14l5', 'Selenbp1', 'Coq10b', 'Coq6', 'Corin', 'Coro2b', 'Sema3c', 'Sema3b', 'Sema3d', 'Sema4a', 'Sema3f', 'Cox6a2', 'Sema5a', 'Sema5b', 'Sema7a', 'Sept4', 'Cpne6', 'Serpinb5', 'Serpinb9', 'Serpine1', 'Serpine3', 'Cr2', 'Crebzf', 'Creld2', 'Sez6l', 'Sf3b4', 'Sfmbt1', 'Crlf2', 'Sfrp1', 'Crocc', 'Sft2d2', 'Sfxn2', 'Sgcz', 'Crybg2', 'Crym', 'Sgsm1', 'Sh2b1', 'Csmd2', 'Csrnp3', 'Sh3gl2', 'Sh3glb2', 'Cstdc2', 'Sh3rf2', 'Sh3rf3', 'Sh3tc2', 'Shank2', 'Ctif', 'Shh', 'Shisa3', 'Shisa6', 'Shmt1', 'Ctsh', 'Shmt2', 'Shprh', 'Shroom2', 'Sik1', 'Cutal', 'Sim2', 'Sipa1l1', 'Sipa1l3', 'Cxcr4', 'Sirt7', 'Six3', 'Six3os1', 'Cyb561', 'Cyb561d1', 'Skida1', 'Cyp2c50', 'Cyp2c55', 'Slc13a3', 'Cyp2c69', 'Slc13a5', 'Slc14a2', 'Slc15a1', 'Slc16a2', 'Cyp4f15', 'Slc17a9', 'Cyp51', 'Cyth1', 'Slc22a12', 'D030046N08Rik', 'Slc22a20', 'D030068K23Rik', 'Slc22a27', 'D130079A08Rik', 'Slc24a4', 'Slc25a19', 'D630003M21Rik', 'Slc25a29', 'Slc25a47', 'Slc26a1', 'Slc26a2', 'Dach1', 'Dancr', 'Slc2a5', 'Dapk1', 'Dapk2', 'Slc30a4', 'Dbil5', 'Dcc', 'Dcdc2a', 'Dcdc2b', 'Slc38a1', 'Slc39a1', 'Ddias', 'Slc45a3', 'Slc46a3', 'Ddx3y', 'Slc5a11', 'Slc5a7', 'Defb39', 'Slc6a7', 'Slc7a2', 'Slc7a3', 'Deptor', 'Derl3', 'Dgat2', 'Slc9b1', 'Dgki', 'Dhcr24', 'Dhcr7', 'Slf1', 'Dhrs3', 'Slitrk2', 'Dhx58os', 'Slx1b', 'Diaph3', 'Dicer1', 'Smad3', 'Dio2', 'Diras2', 'Disc1', 'Dixdc1', 'Dkk3', 'Dlg4', 'Smg8', 'Dlk1', 'Smim22', 'Dlx2', 'Dmp1', 'Dmrta2', 'Smyd1', 'Dnaic1', 'Dnaja4', 'Snhg3', 'Snhg4', 'Snhg9', 'Dnajc27', 'Dnajc30', 'Dnd1', 'Sntg1', 'Doc2a', 'Snx20', 'Dock10', 'Dock11', 'Snx25', 'Dock5', 'Snx30', 'Dok6', 'Dok7', 'Dolpp1', 'Sod3', 'Sorcs3', 'Sowaha', 'Sox13', 'Sox4', 'Drc1', 'Sp5', 'Dusp1', 'Dusp27', 'Dusp6', 'Spatc1l', 'Specc1l', 'Spef2', 'Dynlt1b', 'Spg20', 'Dzank1', 'Spin4', 'Spink2', 'E030042O20Rik', 'E230032D23Rik', 'E230034D01Rik', 'Spsb4', 'Sqle', 'Ece1', 'Echdc2', 'Srcap', 'Srcin1', 'Srd5a3', 'Edrf1', 'Eef1akmt3', 'Eef1akmt4', 'Ssbp3', 'Ssbp4', 'Efcab10', 'Ssc4d', 'Sspo', 'Efcc1', 'Efhd1', 'Efhd2', 'Efna2', 'St3gal2', 'Eftud2', 'St6gal2', 'Egfem1', 'Egfl6', 'St6galnac3', 'St6galnac5', 'Egln3', 'Egr1', 'St8sia3os', 'Ehd2', 'Ehd3', 'Stab2', 'Stambpl1', 'Stat4', 'Stimate', 'Elavl4', 'Elfn2', 'Stk32a', 'Elmo1', 'Elmo2', 'Elmod1', 'Stk39', 'Elovl2', 'Stom', 'Elovl6', 'Emc4', 'Eml2', 'Stx1b', 'Emsy', 'Emx2', 'Emx2os', 'Enkd1', 'Enkur', 'Sulf2', 'Enpp2', 'Sult1a1', 'Sult2a6', 'Enthd1', 'Entpd3', 'Susd4', 'Epha3', 'Epha5', 'Svil', 'Ephb3', 'Swap70', 'Syn3', 'Eps8l1', 'Syne4', 'Syngap1', 'Synj1', 'Ercc3', 'Ercc6l2', 'Syt3', 'Sytl5', 'Esrrb', 'Ess2', 'Tada1', 'Taf13', 'Etv4', 'Etv5', 'Taf7l', 'Eva1b', 'Tafa1', 'Exoc2', 'Extl1', 'Eya1', 'Eya4', 'F2', 'Tbc1d10c', 'F5', 'F630206G17Rik', 'Fads3', 'Fam126b', 'Fam163a', 'Fam163b', 'Fam177a', 'Fam181a', 'Tdrd3', 'Fam193b', 'Tek', 'Fam228a', 'Fam229a', 'Fam3c', 'Fam43b', 'Fam57b', 'Tex22', 'Fam92b', 'Tex9', 'Far1os', 'Fas', 'Tgfbr3', 'Tgfbr3l', 'Tgif1', 'Fbll1', 'Fbln1', 'Fbln5', 'Fbp1', 'Fbxl14', 'Fbxo15', 'Thoc3', 'Ticam2', 'Fbxo9', 'Fcgr2b', 'Fcgr3', 'Fcrls', 'Fdft1', 'Fem1a', 'Tle2', 'Tlnrd1', 'Fgf10', 'Fgf13', 'Fgf20', 'Fgf5', 'Fgf9', 'Fgfr4', 'Tmcc2', 'Fhad1', 'Fhl1', 'Tmeff1', 'Fibp', 'Tmem100', 'Filip1l', 'Firre', 'Tmem123', 'Tmem125', 'Tmem132b', 'Tmem140', 'Flrt1', 'Flrt2', 'Flt3l', 'Flvcr1', 'Fmo1', 'Fn3krp', 'Tmem173', 'Fnbp4', 'Fndc5', 'Tmem181a', 'Tmem191c', 'Folr1', 'Folr2', 'Fos', 'Foxa1', 'Foxa3', 'Tmem204', 'Foxd1', 'Foxd2os', 'Foxg1', 'Tmem203', 'Tmem216', 'Tmem245', 'Frem1', 'Frmpd1os', 'Frmpd4', 'Tmem269', 'Frzb', 'Tmem37', 'Tmem44', 'Tmem50a', 'Fuca1', 'Fuca2', 'Tmem63a', 'Fut4', 'Tmem86a', 'Fzd10', 'Fzd10os', 'Fzd2', 'Fzd4', 'G0s2', 'G630016G05Rik', 'Tmprss6', 'G630093K05Rik', 'Gabra2', 'Gabra6', 'Tnfrsf19', 'Gabrr2', 'Tnfrsf26', 'Tnfsf11', 'Tnfsf12', 'Tnfsf4', 'Tnfsf8', 'Galnt10', 'Tnk2', 'Galnt2', 'Galnt5', 'Tnni3', 'Tns1', 'Gas1', 'Togaram2', 'Gas2l2', 'Tomm6os', 'Gata4', 'Gatc', 'Gatd3a', 'Tor1b', 'Tpd52l1', 'Gc', 'Gcnt1', 'Gcnt4', 'Gcnt7', 'Gdf10', 'Trac', 'Gdpd3', 'Gfm1', 'Traj27', 'Ggnbp1', 'Trcg1', 'Trdn', 'Gigyf1', 'Triap1', 'Gins4', 'Gjb2', 'Gjc2', 'Gjc3', 'Trim34b', 'Gldnos', 'Gle1', 'Gli1', 'Glipr1l3', 'Glipr2', 'Glmp', 'Trim8', 'Glrb', 'Trip6', 'Glt8d2', 'Trmu', 'Trp63', 'Gnmt', 'Gnpda2', 'Gnpnat1', 'Trpv2', 'Got2', 'Tshr', 'Tsix', 'Gpatch4', 'Gpc3', 'Tspan4', 'Tspoap1', 'Gpr141', 'Gpr146', 'Gpr153', 'Gpr155', 'Ttc12', 'Gpr162', 'Gpr37', 'Ttc34', 'Gprasp2', 'Gprc5c', 'Ttll5', 'Gpt2', 'Gprin2', 'Gpx8', 'Gramd4', 'Tubal3', 'Grid2ip', 'Grifin', 'Grik3', 'Grik4', 'Grik5', 'Grin3a', 'Grin3b', 'Grm4', 'Gsdmd', 'Uap1', 'Gssos1', 'Ubash3b', 'Gstt2', 'Gstz1', 'Ube4bos1', 'Ubfd1', 'Ubqln4', 'Ubtd1', 'Ubxn11', 'Gyg', 'Uckl1os', 'Ucma', 'H1f0', 'Uggt2', 'H2-K1', 'Umad1', 'H2-T23', 'Unc13c', 'Unc80', 'Hal', 'Upf1', 'Hap1', 'Hapln1', 'Hccs', 'Ush2a', 'Hdac4', 'Hdc', 'Hdhd5', 'Usp46os2', 'Heg1', 'Usp6nl', 'Hemk1', 'Uty', 'Uvssa', 'Herpud1', 'Hesx1', 'Hexa', 'Heyl', 'Vash1', 'Vasn', 'Vasp', 'Hgh1', 'Higd1b', 'Higd1c', 'Vip', 'Hist1h2ad', 'Vmn2r29', 'Hist1h4j', 'Hist2h2aa1', 'Hmcn1', 'Hmga1b', 'Hmgb2', 'Hmgcr', 'Hmgcs2', 'Vsig4', 'Hmgn5', 'Vstm4', 'Hnf4aos', 'Wars2', 'Hopx', 'Hoxa1', 'Hpcal1', 'Hpse', 'Hpse2', 'Hr', 'Hrh1', 'Wdr45', 'Hsd11b1', 'Wdr72', 'Wdr76', 'Wdr81', 'Hsd17b6', 'Hsf3', 'Hspa12a', 'Whamm', 'Wipf1', 'Wls', 'Wnt6', 'Wnt7a', 'Htr1f', 'Hyou1', 'Icosl', 'Id1', 'Ide', 'Idi1', 'Ier2', 'Yif1a', 'Ifi27', 'Ifi47', 'Yy2', 'Zap70', 'Igdcc3', 'Zbtb3', 'Igf2bp3', 'Igsf21', 'Zc3hav1', 'Il10rb', 'Zcwpw2', 'Il15', 'Il17c', 'Il1rapl2', 'Zfand2a', 'Il22ra1', 'Il31ra', 'Il33', 'Zfp110', 'Il6ra', 'Zfp157', 'Zfp160', 'Impdh2', 'Zfp217', 'Zfp263', 'Inhbc', 'Inka1', 'Zfp319', 'Zfp330', 'Zfp335', 'Insig1', 'Zfp335os', 'Insyn2a', 'Zfp385a', 'Zfp414', 'Iqcc', 'Iqch', 'Zfp451', 'Iqub', 'Irak4', 'Zfp513', 'Zfp516', 'Irf7', 'Zfp524', 'Irs2', 'Zfp536', 'Irx3', 'Irx5', 'Zfp592', 'Zfp629', 'Itgbl1', 'Zfp708', 'Itpka', 'Itpr1', 'Izumo4', 'Jag1', 'Zfp799', 'Zfp808', 'Zfp811', 'Jph2', 'Zfp85', 'Jpx', 'Zfp853', 'Jun', 'Junb', 'Kat2a', 'Zfp936', 'Kat8', 'Katnal2', 'Kbtbd12', 'Kcna2', 'Kcnc4', 'Zfp981', 'Kcng1', 'Kcng2', 'Kcng3', 'Kcng4', 'Zfp991', 'Kcnh3', 'Kcnip1', 'Kcnj3', 'Zic1', 'Zic2', 'Zic4', 'Zic3', 'Zim3', 'Kcnk5', 'Kcnq1', 'Kcns3', 'Kcnt1', 'Kctd12b', 'Kctd17', 'Kdm5d', 'Kdm8', 'Khdrbs3', 'Kif13b', 'Kif19a', 'Kif3c', 'Kifc5b', 'Kirrel2', 'Kiss1', 'Klc1', 'Klc2', 'Klhdc7a', 'Klhl10', 'Klhl25', 'Klre1', 'Krt8', 'Ky', 'L3hypdh', 'L3mbtl4', 'Lamb3', 'Lars2', 'Lca5l', 'Lck', 'Lctl', 'Ldhc', 'Ldlr', 'Lenep', 'Leprot', 'Lgals12', 'Lgr6', 'Lgsn', 'Lhfpl2', 'Lhx2', 'Lipo3', 'Lmo3', 'Lmod1', 'Lonrf2', 'Loxhd1', 'Loxl2', 'Lpar1', 'Lpgat1', 'Lpin2']

subregional_model2.fit(X_uni_to_PRJNA438862, codes)

joblib.dump(
    subregional_model2,
    f"models/svm/best-xgboost-subregional_PRJNA438862_astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pickle",
)

print(X_uni_to_PRJNA438862.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist())

['Lrrc8a', 'Ltbp1', 'Luzp2', 'Malat1', 'Map3k5', 'Mbp', 'Mdga2', '2900097C17Rik', 'Medag', '4833423E24Rik', 'Mfge8', 'Mfsd2a', '4930447C04Rik', 'Msi2', 'Mt1', 'Mt2', 'Mt3', '5031439G07Rik', 'Myoc', 'Nap1l5', 'Nbl1', 'Ndn', 'Ndufa3', 'Nebl', 'Nefl', 'AA465934', 'AC149090.1', 'Nhs', 'AY036118', 'Nnat', 'Acot4', 'Actg1', 'Actn1', 'Nr2f2', 'Nrarp', 'Nrgn', 'Nrsn1', 'Nrxn3', 'Ntm', 'Adcy8', 'Nupr1', 'Agt', 'Ank3', 'Anln', 'Aplp1', 'Apod', 'Apoe', 'App', 'Aqp4', 'Pcsk1n', 'Pde10a', 'Arhgef28', 'Pde4d', 'Arid1a', 'Pdgfd', 'Pdxp', 'Pdzph1', 'Arpp21', 'Pgrmc1', 'Atp1b2', 'Atp5g1', 'Atp5md', 'Atp6v0c', 'Atpif1', 'Pitx2', 'Avp', 'Pkib', 'Pla2g10', 'B230323A14Rik', 'Basp1', 'Bc1', 'Ppia', 'Brinp3', 'Bsg', 'Bst1', 'Btg3', 'Prdx1', 'Camk2a', 'Camk2n1', 'Ptprd', 'Qk', 'Ralyl', 'Rasgrp2', 'Cd24a', 'Cd81', 'Cd9', 'Resp18', 'Cdk8', 'Cebpb', 'Rmst', 'Rnf165', 'Chchd10', 'Ckb', 'Rtn1', 'Rubie', 'Rundc3a', 'Clic3', 'Cmss1', 'Sap30bpos', 'Cntn1', 'Scg5', 'Cops9', 'Selenop', 'Sema3c', 'Cox4i1', 'Cox8a', 'Cpe', 'Sept4', 'Sept7', 'Serf2', 'Sfrp1', 'Sgip1', 'Crym', 'Cst3', 'Ctnna2', 'Shisa6', 'Six3', 'Six3os1', 'Cycs', 'Cyfip2', 'Slc17a6', 'Dbp', 'Dcc', 'Slc6a11', 'Slit2', 'Dmxl1', 'Dnah12', 'Son', 'Sorbs1', 'Dpp10', 'Sparcl1', 'Dync1i1', 'Dynll1', 'Spock1', 'Spock3', 'Ednrb', 'Eef1d', 'Eef1g', 'Efna5', 'Egfem1', 'Egr1', 'Eif1', 'Elmod1', 'Emb', 'Eno1', 'Sulf2', 'Swi5', 'Fabp5', 'Fabp7', 'Tceal9', 'Tenm1', 'Far1os', 'Fbln5', 'Thra', 'Thrsp', 'Timp3', 'Fgfr3', 'Tmem173', 'Fos', 'Fosb', 'Foxg1', 'Fth1', 'Tmsb4x', 'Gas5', 'Gfap', 'Tril', 'Gjc3', 'Gnas', 'Gng3', 'Trpm3', 'Tsc22d3', 'Tsix', 'Gpc5', 'Gpc6', 'Tspan7', 'Ttc3', 'Tubb2a', 'Gria1', 'Gria2', 'Uba52', 'Gstm1', 'Uckl1os', 'H3f3b', 'Uqcr11', 'Uqcrq', 'Usp29', 'Hes5', 'Vim', 'Hmgcs1', 'Hspa8', 'Xist', 'Id2', 'Ywhaz', 'Igf1r', 'Igf2', 'Iqub', 'Irak2', 'Itih3', 'Itm2c', 'Jaml', 'Jun', 'Junb', 'Kcnip4', 'Zic1', 'Zic2', 'Klc1', 'Lars2', 'Ldhb', 'Lgals3', 'Lgi1', 'Lix1']

/opt/python/3.8.8/lib/python3.8/site-packages/sklearn/svm/_base.py:299: ConvergenceWarning:

Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.

Logit

# Generate labels for whole hypothalamus reference dataset
predictions_logit_PRJNA438862 = subregional_model.predict(df_eval2)
adata_eval2.obs["predict_logit_subregion"] = adata_eval2.obs["condit"]
adata_eval2.obs["predict_logit_subregion"] = predictions_logit_PRJNA438862
adata_eval2.obs["predict_logit_subregion"] = pd.Categorical(
    values=adata_eval2.obs["predict_logit_subregion"].astype(str),
    categories=natsorted(
        map(str, np.unique(adata_eval2.obs["predict_logit_subregion"].astype(str)))
    ),
)

PRJNA438862_regulons.obs["predict_logit_subregion"] = adata_eval2.obs["predict_logit_subregion"]

adata_eval2.obsm["X_umap_subfunct"] = adata_eval2.obsm["X_umap"]

reducer_PRJNA438862 = UMAP(
    n_neighbors=ks,
    n_epochs=1000,
    init="spectral",
    learning_rate=0.1,
    min_dist=min_dist,
    spread=spread,
    repulsion_strength=2.0,
    negative_sample_rate=negative_sample_rate,
    densmap=True,
    n_components=2,
    random_state=reseed,
    verbose=False,
)
# reducer_PRJNA438862.fit(
#     adata_eval2.obsm["X_aucell"], adata_eval2.obs["predict_logit_subregion"].astype(str)
# )

reducer_PRJNA438862.fit(
    adata_eval2.to_df().loc[:, X_uni_to_PRJNA438862.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist()], adata_eval2.obs["predict_logit_subregion"].astype(str)
)

UMAP(densmap=True, learning_rate=0.1, min_dist=1.0, n_epochs=1000, n_neighbors=20, negative_sample_rate=10, random_state=42, repulsion_strength=2.0, spread=2, tqdm_kwds={'bar_format': '{desc}: {percentage:3.0f}%| {bar} {n_fmt}/{total_fmt} [{elapsed}]', 'desc': 'Epochs completed', 'disable': True})

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# adata_eval2.obsm["X_umap_logit"] = reducer_PRJNA438862.transform(adata_eval2.obsm["X_aucell"])
adata_eval2.obsm["X_umap_logit"] = reducer_PRJNA438862.transform(adata_eval2.to_df().loc[:, X_uni_to_PRJNA438862.columns[np.array(subregional_model[1].get_support(indices=True), dtype=int)].tolist()])
PRJNA438862_regulons.obsm["X_umap_logit"] = adata_eval2.obsm["X_umap_logit"]

adata_eval2.write(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA438862-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)
adata_eval2.write_loom(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA438862-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.loom"
)
pd.DataFrame(adata_eval2.obsm["X_umap_logit"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/best_logit-subregional_PRJNA438862-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-logit.tsv",
    sep="\t",
    header=True,
)
pd.DataFrame(adata_eval2.obsm["X_umap_subfunct"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/PRJNA438862-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-subfunct.tsv",
    sep="\t",
    header=True,
)

The loom file will lack these fields:
{'X_pca', 'ora_pvals', 'X_aucell', 'X_umap', 'ora_estimate', 'X_umap_logit', 'X_pacmap', 'X_umap_subfunct'}
Use write_obsm_varm=True to export multi-dimensional annotations

save_PRJNA438862 = f"-best_logit-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pdf"

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=["predict_logit_subregion", "k_tree"],
    title=f"UMAP supervised subfunction: PRJNA438862 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save=save_PRJNA438862)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-best_logit-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=["predict_logit_subregion", "k_tree"],
    title=f"UMAP supervised density logit prediction: PRJNA438862 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-predict_logit_subregion" + save_PRJNA438862,
)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-predict_logit_subregion-best_logit-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

XGBoost

# Generate labels for whole hypothalamus reference dataset
predictions_xgboost_PRJNA438862 = subregional_model2.predict(df_eval2)
adata_eval2.obs["predict_xgboost_subregion"] = adata_eval2.obs["condit"]
adata_eval2.obs["predict_xgboost_subregion"] = predictions_xgboost_PRJNA438862
adata_eval2.obs["predict_xgboost_subregion"] = pd.Categorical(
    values=adata_eval2.obs["predict_xgboost_subregion"].astype(str),
    categories=natsorted(
        map(str, np.unique(adata_eval2.obs["predict_xgboost_subregion"].astype(str)))
    ),
)

PRJNA438862_regulons.obs["predict_xgboost_subregion"] = adata_eval2.obs["predict_xgboost_subregion"]

reducer_PRJNA438862 = UMAP(
    n_neighbors=ks,
    n_epochs=1000,
    init="spectral",
    learning_rate=0.1,
    min_dist=min_dist,
    spread=spread,
    repulsion_strength=2.0,
    negative_sample_rate=negative_sample_rate,
    densmap=True,
    n_components=2,
    random_state=reseed,
    verbose=False,
)
# reducer_PRJNA438862.fit(
#     adata_eval2.obsm["X_aucell"],
#     adata_eval2.obs["predict_xgboost_subregion"].astype(str)
# )

reducer_PRJNA438862.fit(
    adata_eval2.to_df().loc[:, X_uni_to_PRJNA438862.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist()], adata_eval2.obs["predict_xgboost_subregion"].astype(str)
)

UMAP(densmap=True, learning_rate=0.1, min_dist=1.0, n_epochs=1000, n_neighbors=20, negative_sample_rate=10, random_state=42, repulsion_strength=2.0, spread=2, tqdm_kwds={'bar_format': '{desc}: {percentage:3.0f}%| {bar} {n_fmt}/{total_fmt} [{elapsed}]', 'desc': 'Epochs completed', 'disable': True})

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# adata_eval2.obsm["X_umap"] = reducer_PRJNA438862.transform(adata_eval2.obsm["X_aucell"])
adata_eval2.obsm["X_umap"] = reducer_PRJNA438862.transform(adata_eval2.to_df().loc[:, X_uni_to_PRJNA438862.columns[np.array(subregional_model2[1].get_support(indices=True), dtype=int)].tolist()])
PRJNA438862_regulons.obsm["X_umap_xgboost"] = adata_eval2.obsm["X_umap"]

adata_eval2.write(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA438862-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)
adata_eval2.write_loom(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA438862-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.loom"
)
pd.DataFrame(adata_eval2.obsm["X_umap"], index=adata_eval2.obs_names).to_csv(
    f"/data/data/resolved_subregions_by_microclusters/best_xgboost-subregional_PRJNA438862-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}-umap-xgboost.tsv",
    sep="\t",
    header=True,
)

The loom file will lack these fields:
{'X_pca', 'ora_pvals', 'X_aucell', 'X_umap', 'ora_estimate', 'X_umap_logit', 'X_pacmap', 'X_umap_subfunct'}
Use write_obsm_varm=True to export multi-dimensional annotations

save_PRJNA438862 = f"-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.pdf"

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=["predict_xgboost_subregion", "k_tree"],
    title=f"UMAP supervised subfunction: PRJNA438862 (amb.FPR={cb_fpr}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save=save_PRJNA438862,
)

WARNING: The title list is shorter than the number of panels. Using 'color' value instead for some plots.

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

sc.pl.umap(
    adata_eval2,
    color=["predict_xgboost_subregion"],
    title=f"UMAP: supervised density XGBoost, PRJNA438862 \n(amb.FPR={cb_fpr}, k={k}, MSP={connectivity_model}, \nmetric={metric}, n_genes_excluded={signature})",
    add_outline=True,
    legend_fontsize=12,
    legend_fontoutline=2,
    frameon=False,
    palette="nipy_spectral",
    show=True,
    save="-predict_xgboost_subregion" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-predict_xgboost_subregion-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:369: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:379: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/opt/python/3.8.8/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

npepdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in npep if g in adata_eval2.var_names])]
npepdf.describe()
npepcols = [col for col in npepdf.columns if npepdf[col].quantile(0.985) > 1]

for gene in npepcols:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        vmax="p98.5",
        na_color="#9A9FB080",
        save="-npep-" + gene + save_PRJNA438862,
)

nprdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in npr if g in adata_eval2.var_names])]
nprdf.describe()

	Adcyap1r1	Avpr1a	Calcr	Calcrl	Cckar	Cckbr	Cntfr	Crhr1	Crhr2	Esr1	...	Sstr1	Sstr2	Sstr3	Tacr1	Tacr3	Trhr	Trhr2	Tshr	Vipr1	Vipr2
count	4313.000000	4313.000000	4313.0	4313.000000	4313.000000	4313.000000	4313.000000	4313.0	4313.0	4313.000000	...	4313.000000	4313.000000	4313.0	4313.000000	4313.000000	4313.000000	4313.0	4313.000000	4313.000000	4313.000000
mean	0.493624	0.000232	0.0	0.002319	0.000232	0.000696	0.188964	0.0	0.0	0.001623	...	0.000927	0.000464	0.0	0.000464	0.000464	0.004173	0.0	0.002319	0.000464	0.001391
std	0.900174	0.015227	0.0	0.048101	0.015227	0.034045	0.506222	0.0	0.0	0.045657	...	0.030443	0.021532	0.0	0.021532	0.021532	0.064475	0.0	0.048101	0.021532	0.052735
min	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.000000	...	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.000000	...	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.000000	...	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000
75%	1.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.000000	...	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000
max	26.000000	1.000000	0.0	1.000000	1.000000	2.000000	12.000000	0.0	0.0	2.000000	...	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	3.000000

8 rows × 66 columns

nprdf.quantile(q=[0.5, 0.9, 0.985, 1.0], axis=0)

	Adcyap1r1	Avpr1a	Calcr	Calcrl	Cckar	Cckbr	Cntfr	Crhr1	Crhr2	Esr1	...	Sstr1	Sstr2	Sstr3	Tacr1	Tacr3	Trhr	Trhr2	Tshr	Vipr1	Vipr2
0.500	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.900	2.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.985	3.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1.000	26.0	1.0	0.0	1.0	1.0	2.0	12.0	0.0	0.0	2.0	...	1.0	1.0	0.0	1.0	1.0	1.0	0.0	1.0	1.0	3.0

4 rows × 66 columns

nprcols = [col for col in nprdf.columns if nprdf[col].quantile(0.985) > 1]
nprcols3 = [col for col in nprdf.columns if col not in nprcols and nprdf[col].max() == 1]
nprcols2 = [col for col in nprdf.columns if col not in nprcols and col not in nprcols3]
print(nprcols)
print(nprcols2)
print(nprcols3)

['Adcyap1r1', 'Cntfr', 'Ghr', 'Igf1r', 'Ntrk2', 'Ntsr2']
['Calcr', 'Cckbr', 'Crhr1', 'Crhr2', 'Esr1', 'Galr3', 'Ghsr', 'Glp1r', 'Gpr83', 'Gpr149', 'Hcrtr2', 'Insr', 'Mc3r', 'Mc4r', 'Mchr1', 'Nmur2', 'Npffr2', 'Npr2', 'Npr3', 'Npy2r', 'Npy5r', 'Oprd1', 'Oprk1', 'Prlhr', 'Prlr', 'Prokr2', 'Sstr3', 'Trhr2', 'Vipr2']
['Avpr1a', 'Calcrl', 'Cckar', 'Galr1', 'Gpr55', 'Grpr', 'Hcrtr1', 'Insrr', 'Kiss1r', 'Lepr', 'Mc1r', 'Nmbr', 'Nmur1', 'Npffr1', 'Npr1', 'Npsr1', 'Npy1r', 'Ntsr1', 'Oprl1', 'Oprm1', 'Oxtr', 'Qrfpr', 'Rxfp1', 'Rxfp2', 'Sstr1', 'Sstr2', 'Tacr1', 'Tacr3', 'Trhr', 'Tshr', 'Vipr1']

sc.pl.umap(
    adata_eval2,
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nprcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-npr2" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr2-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nprcols3,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    na_color="#9A9FB080",
    save="-npr3" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-npr3-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

nmrdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in nmr if g in adata_eval2.var_names])]
nmrdf.describe()

	Adra1a	Adra1b	Adra2a	Adra2b	Adra2c	Adrb1	Adrb2	Adrb3	Adrm1	Adora1	...	P2rx7	P2ry1	P2ry2	P2ry6	P2ry12	P2ry13	P2ry14	Ryr1	Ryr2	Ryr3
count	4313.000000	4313.000000	4313.000000	4313.0	4313.000000	4313.000000	4313.0	4313.000000	4313.000000	4313.000000	...	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000
mean	0.057037	0.012288	0.063993	0.0	0.000696	0.019244	0.0	0.000232	0.036865	0.110828	...	0.038952	0.019476	0.000696	0.000232	0.003710	0.000696	0.028055	0.001159	0.057269	0.006260
std	0.267248	0.418814	0.293062	0.0	0.026368	0.142372	0.0	0.015227	0.201535	0.386766	...	0.225598	0.171188	0.034045	0.015227	0.064503	0.045681	0.174702	0.034032	0.294844	0.087257
min	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	5.000000	27.000000	8.000000	0.0	1.000000	2.000000	0.0	1.000000	2.000000	6.000000	...	4.000000	4.000000	2.000000	1.000000	2.000000	3.000000	3.000000	1.000000	8.000000	2.000000

8 rows × 136 columns

nmrcols = [col for col in nmrdf.columns if nmrdf[col].quantile(0.985) > 1]
nmrcols3 = [col for col in nmrdf.columns if col not in nmrcols and nmrdf[col].max() == 1]
nmrcols2 = [col for col in nmrdf.columns if col not in nmrcols and col not in nmrcols3]
print(nmrcols)
print(nmrcols2)
print(nmrcols3)

['Gria1', 'Gria2', 'Grid1', 'Grid2', 'Grin3a', 'Grm5', 'Grm3', 'Gabrb1', 'Gabrg1', 'Gabrg3', 'Gabbr1', 'Gabbr2', 'Gnas', 'Gnai2', 'Gnao1', 'Gnaq', 'Gna12', 'Gna13', 'Gnb1', 'Gnb2', 'Gng5', 'Gng12']
['Adra1a', 'Adra1b', 'Adra2a', 'Adra2b', 'Adrb1', 'Adrb2', 'Adrm1', 'Adora1', 'Adora2b', 'Adora3', 'Chrm2', 'Chrm4', 'Chrm5', 'Chrna1', 'Chrna2', 'Chrna3', 'Chrna5', 'Chrna6', 'Chrna7', 'Chrnb1', 'Gria3', 'Gria4', 'Grik1', 'Grik2', 'Grik3', 'Grik4', 'Grik5', 'Grin1', 'Grin2a', 'Grin2b', 'Grin2c', 'Grm2', 'Grm4', 'Grm7', 'Grm8', 'Gabra1', 'Gabra2', 'Gabra4', 'Gabrb2', 'Gabrb3', 'Gabrr1', 'Gabrr2', 'Gabrr3', 'Drd2', 'Htr1f', 'Htr2a', 'Htr2c', 'Htr3a', 'Htr3b', 'Htr5a', 'Gnai1', 'Gnai3', 'Gna11', 'Gnal', 'Gnb4', 'Gnb5', 'Gng2', 'Gng7', 'Gng10', 'Gng11', 'Gngt2', 'P2rx1', 'P2rx2', 'P2rx3', 'P2rx4', 'P2rx5', 'P2rx6', 'P2rx7', 'P2ry1', 'P2ry2', 'P2ry12', 'P2ry13', 'P2ry14', 'Ryr2', 'Ryr3']
['Adra2c', 'Adrb3', 'Adora2a', 'Chrm1', 'Chrm3', 'Chrna4', 'Chrna9', 'Chrna10', 'Chrnb2', 'Chrnb3', 'Grin2d', 'Grin3b', 'Grm1', 'Gabra3', 'Gabra5', 'Gabra6', 'Gabrg2', 'Gabrd', 'Gabre', 'Gabrp', 'Gabrq', 'Drd1', 'Drd3', 'Drd5', 'Htr1a', 'Htr1b', 'Htr1d', 'Htr2b', 'Htr4', 'Htr5b', 'Htr6', 'Htr7', 'Gnb3', 'Gng3', 'Gng4', 'Gng8', 'Gng13', 'P2ry6', 'Ryr1']

sc.pl.umap(
    adata_eval2,
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nmrcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-nmr2" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr2-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=nmrcols3,
    frameon=False,
    show=True,
    size=40,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    vmin=0.0,
    na_color="#9A9FB080",
    save="-nmr3" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-nmr3-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

adddf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in genes_embed if g in adata_eval2.var_names])]
adddf.describe()

	Abcd1	Abcd2	Abcd3	Acaa2	Acox1	Agt	Aldh1a1	Aldh1l1	Aldoc	Apoe	...	Slc4a4	Slc6a11	Slc7a10	Slc38a1	Slit1	Slit2	Sox9	Tafa1	Tkt	Trpv1
count	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	...	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000
mean	0.008579	0.131231	0.177139	0.065847	0.137259	1.086483	0.028982	0.185022	2.591700	13.877348	...	2.298168	4.555994	0.616045	0.027127	0.001159	0.077904	0.428008	0.050777	0.157431	0.000232
std	0.092234	0.385174	0.468553	0.274664	0.409404	1.488261	0.227604	0.481174	2.637938	9.833164	...	2.491198	3.654639	0.880126	0.251077	0.040274	0.322266	0.865744	0.259280	0.434511	0.015227
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	7.000000	...	1.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	2.000000	12.000000	...	2.000000	4.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	4.000000	18.000000	...	3.000000	6.000000	1.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000
max	1.000000	4.000000	5.000000	3.000000	4.000000	32.000000	6.000000	8.000000	27.000000	106.000000	...	52.000000	55.000000	8.000000	5.000000	2.000000	3.000000	27.000000	5.000000	7.000000	1.000000

8 rows × 78 columns

agcols = [col for col in adddf.columns if adddf[col].quantile(0.985) > 1]
agcols3 = [col for col in adddf.columns if col not in agcols and adddf[col].max() == 1]
agcols2 = [col for col in adddf.columns if col not in agcols and col not in agcols3]
print(agcols)
print(agcols2)
print(agcols3)

['Abcd3', 'Agt', 'Aldh1l1', 'Aldoc', 'Apoe', 'Aqp4', 'Ckb', 'Cst3', 'Egfr', 'Eno1', 'Fgfr3', 'Fis1', 'Fth1', 'Ftl1', 'Gfap', 'Gja1', 'Glul', 'Hacd2', 'Hepacam', 'Htra1', 'Lcat', 'Lxn', 'Mgll', 'Ndrg2', 'Nfia', 'Pla2g7', 'Plcb1', 'Pygb', 'S100b', 'Scd2', 'Sgcd', 'Slc1a2', 'Slc1a3', 'Slc4a4', 'Slc6a11', 'Slc7a10', 'Sox9', 'Tkt']
['Abcd2', 'Acaa2', 'Acox1', 'Aldh1a1', 'Cnr1', 'Cnr2', 'Dagla', 'Daglb', 'Dcc', 'Decr2', 'Ech1', 'Fos', 'Gli1', 'Hadhb', 'Lpcat3', 'Mfn1', 'Mfn2', 'Mief1', 'Napepld', 'Opa1', 'Otp', 'Pex1', 'Pex10', 'Pex12', 'Pex13', 'Pex14', 'Pex16', 'Pex2', 'Pex26', 'Pex3', 'Pex6', 'S100a6', 'Slc38a1', 'Slit1', 'Slit2', 'Tafa1']
['Abcd1', 'Dnm1', 'Faah', 'Trpv1']

sc.pl.umap(
    adata_eval2,
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=agcols2,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    na_color="#9A9FB080",
    save="-adgen2" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen2-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.umap(
    adata_eval2,
    color=agcols3,
    frameon=False,
    show=True,
    size=40,
    cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
    vmin=0.0,
    na_color="#9A9FB080",
    save="-adgen3" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-adgen3-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

tfdf = pd.DataFrame(adata_eval2.raw.X.todense(), columns=adata_eval2.var_names, index=adata_eval2.obs_names).loc[:, np.array([g for g in tf_genes if g in adata_eval2.var_names])]
tfdf.describe()

	Aff3	Alx1	Arid1a	Arid1b	Arid2	Arid3a	Arid3b	Arid3c	Arid4a	Arid4b	...	Caskin1	Gtf3c4	Gcdh	BC024139	Zfp398	Tcfl5	A630089N07Rik	Phf8	Zhx3	Zfp82
count	4313.000000	4313.0	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	...	4313.000000	4313.000000	4313.000000	4313.000000	4313.000000	4313.0	4313.000000	4313.000000	4313.000000	4313.000000
mean	0.099003	0.0	0.096684	0.113146	0.106886	0.000696	0.005796	0.000464	0.076745	0.095989	...	0.100858	0.013216	0.029678	0.000232	0.041734	0.0	0.079063	0.020635	0.209367	0.003478
std	0.352807	0.0	0.326150	0.367633	0.539215	0.034045	0.075922	0.021532	0.454211	0.354621	...	0.434250	0.114211	0.176416	0.015227	0.224067	0.0	0.460408	0.145402	0.506438	0.062693
min	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
75%	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
max	6.000000	0.0	4.000000	7.000000	22.000000	2.000000	1.000000	1.000000	22.000000	9.000000	...	17.000000	1.000000	2.000000	1.000000	3.000000	0.0	17.000000	2.000000	4.000000	2.000000

8 rows × 1347 columns

tfdf.quantile(q=[0.5, 0.9, 0.985, 1.0], axis=0)

	Aff3	Alx1	Arid1a	Arid1b	Arid2	Arid3a	Arid3b	Arid3c	Arid4a	Arid4b	...	Caskin1	Gtf3c4	Gcdh	BC024139	Zfp398	Tcfl5	A630089N07Rik	Phf8	Zhx3	Zfp82
0.500	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.900	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
0.985	1.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	1.0	1.0	...	1.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	2.0	0.0
1.000	6.0	0.0	4.0	7.0	22.0	2.0	1.0	1.0	22.0	9.0	...	17.0	1.0	2.0	1.0	3.0	0.0	17.0	2.0	4.0	2.0

4 rows × 1347 columns

tfcols = [col for col in tfdf.columns if tfdf[col].quantile(0.9) > 3]
tfcols3 = [col for col in tfdf.columns if col not in tfcols and tfdf[col].max() == 1]
tfcols2 = [col for col in tfdf.columns if col not in tfcols and tfdf[col].max() > 1]

print(tfcols)
print(tfcols2)
print(tfcols3)

['Rora', 'Npas3']
['Aff3', 'Arid1a', 'Arid1b', 'Arid2', 'Arid3a', 'Arid4a', 'Arid4b', 'Arid5b', 'Bcl6', 'Bdp1', 'Btaf1', 'Bag1', 'Bcor', 'Brf1', 'Bach1', 'Bach2', 'Bmi1', 'Ctbp1', 'Ctbp2', 'C1d', 'Cebpa', 'Cebpb', 'Cebpd', 'Cebpg', 'Cebpz', 'Ctcf', 'Cnot3', 'Cops2', 'Crebbp', 'Cited2', 'Dbp', 'Dpf2', 'Ddx5', 'Ddx3x', 'Ddit3', 'Drap1', 'Ep300', 'Ets1', 'Ets2', 'E2f1', 'E2f3', 'E2f4', 'E2f5', 'E2f6', 'E4f1', 'Elf1', 'Elf2', 'Elf5', 'Elk1', 'Elk3', 'Elk4', 'Eaf2', 'Eid1', 'Eid2', 'Erf', 'Ewsr1', 'Fos', 'Daxx', 'Gabpa', 'Gata4', 'Gatad2b', 'Gli1', 'Gli2', 'Gli3', 'Glis2', 'Herc1', 'Ikzf1', 'Isl1', 'Irx3', 'Kat2a', 'Kat2b', 'Kat5', 'Kat6a', 'Klf10', 'Klf12', 'Klf13', 'Klf15', 'Klf16', 'Klf4', 'Klf5', 'Klf7', 'Klf9', 'Kcnip3', 'Lpp', 'Lhx2', 'Mxd1', 'Mxi1', 'Mlx', 'Mecom', 'Mybbp1a', 'Mycbp', 'Maz', 'Max', 'Meis1', 'Meis2', 'Mdfi', 'Nkx2-1', 'Nkx6-2', 'Nab2', 'Paxip1', 'Pou2f1', 'Pou2f2', 'Pou3f1', 'Pou3f2', 'Pou3f4', 'Pou6f1', 'Patz1', 'Pknox2', 'Pknox1', 'Rab11fip3', 'Rorb', 'Rbak', 'Rest', 'Rybp', 'Rrn3', 'Arhgap35', 'Sertad2', 'Smyd1', 'Smyd3', 'Setdb1', 'Skil', 'Smad3', 'Smad5', 'Smad7', 'Smad9', 'Snw1', 'Spen', 'Sox1', 'Sox10', 'Sox11', 'Sox13', 'Sox18', 'Sox2', 'Sox21', 'Sox3', 'Sox4', 'Sox5', 'Sox6', 'Sox8', 'Sox9', 'Sub1', 'Smarcal1', 'Smarca1', 'Smarca2', 'Smarca4', 'Smarca5', 'Smarcb1', 'Smarcc1', 'Smarcd1', 'Smarcd2', 'Smarcd3', 'Smarce1', 'Smarcad1', 'Son', 'Tbx2', 'Tbpl1', 'Tmf1', 'Taf1', 'Taf10', 'Taf11', 'Taf12', 'Taf2', 'Taf3', 'Taf4b', 'Taf6l', 'Taf6', 'Taf7', 'Taf8', 'Taf9', 'Taf1a', 'Taf1c', 'Tead1', 'Tead2', 'Tead3', 'Traf4', 'Tsc22d1', 'Snrpc', 'Snrpb2', 'Wdr5', 'Wwtr1', 'Wwox', 'Xbp1', 'Ybx1', 'Yy1', 'Ascl1', 'Ascc1', 'Atf2', 'Atf4', 'Atf5', 'Atf6', 'Atf7ip', 'Atf7', 'Ar', 'Aatf', 'Apex1', 'Arx', 'Arnt2', 'Arnt', 'Arntl', 'Ahr', 'Atr', 'Atm', 'Relb', 'Bhlhe40', 'Bhlhe41', 'Blzf1', 'Bmp7', 'Bin1', 'Bptf', 'Brd4', 'Brd7', 'Abl1', 'Creb1', 'Creb3', 'Creb3l1', 'Crem', 'Camta2', 'Cic', 'Ctnnb1', 'Cdc5l', 'Cdc6', 'Creg1', 'Clock', 'Cbfa2t3', 'Cux1', 'Cux2', 'Dmtf1', 'Ccnt1', 'Ccnt2', 'Cdk7', 'Cdk8', 'Cdk9', 'Cdkn1a', 'Ddb1', 'Ddb2', 'Dcp1a', 'Dtx1', 'Dlx2', 'Dlx6', 'Ebf1', 'Egr1', 'Eed', 'Emx2', 'Epas1', 'En1', 'Ezh1', 'Ezh2', 'Esr1', 'Esrra', 'Esrrg', 'Etv1', 'Etv3', 'Etv4', 'Etv5', 'Foxg1', 'Foxj2', 'Foxk1', 'Foxo1', 'Foxo3', 'Foxo4', 'Foxo6', 'Foxp1', 'Foxp2', 'Fosl1', 'Fus', 'Gtf2h1', 'Gtf2h2', 'Gtf2h4', 'Gtf2ird1', 'Gtf2i', 'Gtf2b', 'Gtf3a', 'Gtf3c1', 'Gcm1', 'Gmeb1', 'Gmeb2', 'Grhl1', 'Hes5', 'Hes6', 'Hes7', 'Hey1', 'Hsf1', 'Hsbp1', 'Hltf', 'Hlf', 'Hnrnpab', 'Hnrnpc', 'Hnrnpdl', 'Hnrnph2', 'Hnrnpk', 'Hnrnpl', 'Hnrnpll', 'Hnrnpu', 'Hmgb1', 'Hmgb2', 'Hmgn1', 'Hmgn3', 'Hdac1', 'Hdac10', 'Hdac2', 'Hdac3', 'Hdac4', 'Hdac5', 'Hdac6', 'Hdac7', 'Hdac8', 'Hdac9', 'Hoxa1', 'Hcfc1', 'Hivep1', 'Hivep2', 'Hif1a', 'Id1', 'Id2', 'Id3', 'Id4', 'Itgb3bp', 'Irf1', 'Irf2bp1', 'Irf2', 'Irf3', 'Irf7', 'Irf9', 'Jade1', 'Junb', 'Jund', 'Jun', 'Lztfl1', 'Lcor', 'Lef1', 'Kdm5a', 'Kdm5b', 'Kdm5c', 'Kmt2a', 'Maml1', 'Maml3', 'Mnt', 'Med1', 'Med12', 'Med14', 'Med15', 'Med17', 'Med21', 'Med23', 'Med24', 'Med6', 'Mnat1', 'Mtf1', 'Mtf2', 'Mta1', 'Mta2', 'Mecp2', 'Mbd1', 'Mbd2', 'Mitf', 'Mapk14', 'Mapk3', 'Mapk7', 'Msx2', 'Myef2', 'Myt1', 'Myt1l', 'Mzf1', 'Mllt1', 'Mllt3', 'Mllt6', 'Mef2a', 'Mef2b', 'Mef2d', 'Nrg1', 'Npas2', 'Notch1', 'Notch2', 'Notch3', 'Nfia', 'Nfic', 'Nfix', 'Nfat5', 'Nfatc1', 'Nfatc3', 'Nfkb1', 'Nfkbia', 'Nfkbie', 'Nfkbiz', 'Nfe2l1', 'Nufip1', 'Ncor1', 'Ncor2', 'Ncoa1', 'Ncoa2', 'Ncoa3', 'Ncoa4', 'Ncoa5', 'Ncoa6', 'Ncoa7', 'Nrip1', 'Nr1d1', 'Nr1d2', 'Nr1h2', 'Nr1h3', 'Nr2c1', 'Nr2c2', 'Nr2e1', 'Nr2f1', 'Nr2f2', 'Nr2f6', 'Nr3c1', 'Nr3c2', 'Nr4a1', 'Nr6a1', 'Nsd1', 'Nrf1', 'Nfx1', 'Nfya', 'Nfyb', 'Npm1', 'Olig1', 'Olig2', 'Otx2', 'Pax6', 'Prrx1', 'Ptch1', 'Per1', 'Per2', 'Per3', 'Ppargc1b', 'Ppara', 'Ppard', 'Pgs1', 'Plag1', 'Plagl2', 'Parp1', 'Parp12', 'Pabpn1', 'Pcbp1', 'Pcbp2', 'Pcbp4', 'Pbrm1', 'Pcgf6', 'Pqbp1', 'Polr2a', 'Polr2b', 'Polr2k', 'Pbx1', 'Pbx3', 'Pbx4', 'Pdcd7', 'Phb2', 'Preb', 'Pml', 'Psmc5', 'Pin1', 'Pias1', 'Pias2', 'Pias3', 'Pias4', 'Pura', 'Purb', 'Phtf1', 'Rreb1', 'Rbpj', 'Rsl1', 'Rfx2', 'Rfx4', 'Rfx5', 'Rfxank', 'Rfxap', 'Rel', 'Rax', 'Rb1', 'Rbbp4', 'Rbbp7', 'Rbl1', 'Rara', 'Rarb', 'Rarg', 'Rxra', 'Rxrb', 'Rpl7', 'Rpl7a', 'Ring1', 'Rnf14', 'Rnf2', 'Rnf25', 'Rnf4', 'Runx2', 'Runx1t1', 'Sall1', 'Sall2', 'Sall3', 'Sall4', 'Safb', 'Safb2', 'Scrt1', 'Srf', 'Scmh1', 'Stat1', 'Stat3', 'Stat5a', 'Stat5b', 'Six3', 'Sirt3', 'Ski', 'Snapc4', 'Satb1', 'Syne1', 'Spi1', 'Sra1', 'Srebf2', 'Srebf1', 'Strn', 'Ssrp1', 'Supt16', 'Supt4a', 'Supt5', 'Suv39h1', 'Suv39h2', 'Suz12', 'Tdg', 'Tox', 'Thra', 'Thrap3', 'Thrb', 'Trip11', 'Tef', 'Topors', 'Sp1', 'Sp3', 'Sp4', 'Tcf12', 'Tcf20', 'Tcf3', 'Tcf4', 'Tcf7l1', 'Tcf7l2', 'Tfam', 'Tfcp2', 'Tfdp1', 'Tfdp2', 'Tfe3', 'Ttf1', 'Sin3a', 'Sin3b', 'Tbl1x', 'Tle1', 'Tle2', 'Tle3', 'Tle4', 'Tle6', 'Trp53', 'Trp63', 'Trrap', 'Mdm2', 'Mdm4', 'Tsn', 'Trps1', 'Tgs1', 'Trim24', 'Trim28', 'Trim30a', 'Trim33', 'Tsc2', 'Ubp1', 'Ubtf', 'Usf1', 'Usf2', 'Mafb', 'Mafg', 'Rela', 'Vps72', 'Vezf1', 'Vax1', 'Vhl', 'Zrsr1', 'Zc3h8', 'Zeb1', 'Zeb2', 'Zbtb14', 'Zbtb16', 'Zbtb17', 'Zbtb20', 'Zbtb33', 'Zbtb7b', 'Zscan21', 'Zfp106', 'Zfp110', 'Zfp148', 'Zfp239', 'Zfp263', 'Zfp326', 'Zfp422', 'Zfp423', 'Zfp467', 'Zfp503', 'Zfp60', 'Zfp64', 'Zfp668', 'Zfp68', 'Zik1', 'Zic1', 'Zic3', 'Zic4', 'Zic5', 'Zfpm1', 'Zfpm2', 'Zkscan17', 'Zmym2', 'Zhx1', 'Zhx2', 'Adnp', 'Aebp2', 'Aip', 'Alx4', 'Ank1', 'Ankfy1', 'Atf1', 'Zfp36l1', 'Cbfb', 'Cbx3', 'Cbx4', 'Ccnk', 'Cdk2', 'Cdk4', 'Cdkn2d', 'Chgb', 'Dach1', 'Dnmt1', 'Dnmt3a', 'Dr1', 'Drg1', 'Ell', 'Ercc2', 'Ercc3', 'Erg', 'Etv6', 'Bcl11a', 'Fah', 'Fem1a', 'Fem1b', 'Foxn2', 'Gabpb1', 'Gas7', 'Mtpn', 'Baz1a', 'Hcls1', 'Hes1', 'Hey2', 'Foxj1', 'Hipk2', 'Hira', 'Hmgn2', 'Hmg20b', 'Hmgb3', 'Hsf2', 'Htr5a', 'Elavl2', 'Ifnar2', 'Ikbkb', 'Ikbkg', 'Ilf3', 'Invs', 'Irf4', 'Irx1', 'Irx2', 'Klf2', 'Klf3', 'Hivep3', 'Ldb1', 'Ldb2', 'Lmo2', 'Lmo4', 'Maf', 'Mbd3', 'Mcm4', 'Mcm6', 'Mcm7', 'Mef2c', 'Rab8a', 'Men1', 'Mid1', 'Mllt10', 'Nab1', 'Naca', 'Ndn', 'Nedd8', 'Nfatc2ip', 'Nfe2l2', 'Nfib', 'Nfkbib', 'Nfkbil1', 'Nfyc', 'Nkx2-2', 'Notch4', 'Sqstm1', 'Otx1', 'Pbx2', 'Pdcd11', 'Enpp2', 'Peg3', 'Phf2', 'Papola', 'Cnot7', 'Pou3f3', 'Ppargc1a', 'Scand1', 'Ppp1r1b', 'Ppp3cb', 'Ppp5c', 'Prkar1a', 'Prox1', 'Psmc3', 'Ptma', 'Rab11b', 'Rab18', 'Rai1', 'Rbl2', 'Rfx3', 'Rnps1', 'Polr2c', 'Polr2j', 'Ruvbl2', 'Rxrg', 'Khdrbs1', 'Sap18', 'Ostf1', 'Siah2', 'Six5', 'Ighmbp2', 'Snrpb', 'Sox12', 'Zfp143', 'Stag1', 'Stat2', 'Strap', 'Taf1b', 'Tbp', 'Tbx15', 'Tcea1', 'Phf1', 'Thrsp', 'Tial1', 'Tmpo', 'Dedd', 'Ppp1r13b', 'Trip6', 'Tob1', 'Trp73', 'Tsg101', 'Baz1b', 'Zfp1', 'Zfp105', 'Zfp13', 'Zfp2', 'Zfp207', 'Zfp27', 'Zfp35', 'Zfp36', 'Zfp37', 'Zfp40', 'Zfp41', 'Zfp46', 'Zfp51', 'Zfp52', 'Zfp61', 'Zfp9', 'Zfp93', 'Zfx', 'Ash2l', 'Bpnt1', 'Dido1', 'Dazap2', 'Ing1', 'Map3k12', 'Mapk1', 'Mapk8', 'Zfp146', 'Cops5', 'Polr3e', 'Sh3d19', 'Zfp275', 'Trp53bp1', 'Ing4', 'Limd1', 'Mga', 'Zfp292', 'Pdlim4', 'Zfp354c', 'Abt1', 'Cbx8', 'Fbxw7', 'Nrbf2', 'Lsm4', 'Keap1', 'Ppp2r1a', 'Ccnc', 'Ppp1r10', 'Carhsp1', 'Plrg1', 'Pla2g6', 'Psmd10', 'Hif3a', 'Rab11a', 'Ddx20', 'Deaf1', 'Irx5', 'Tcerg1', 'Heyl', 'Zfp386', 'Rbm14', 'Nupr1', 'Zfp113', 'Trip4', 'Papolb', 'Zfp235', 'Rps6ka4', 'Zfp111', 'Usp49', 'Zfp109', 'Zfp112', 'Jmy', 'Hic2', 'Carm1', 'Sap30', 'Foxb1', 'Sirt2', 'Sp5', 'Nmi', 'Asb1', 'Asb3', 'Znrd1', 'Dmap1', 'Polr2e', 'Polr2l', 'Zmynd11', 'Mcm8', 'Ctnnbl1', 'Ccnh', 'Hmg20a', 'Ssbp2', 'Zdhhc6', 'Yaf2', 'Polr3d', 'Nrarp', 'Rnf141', 'Psmd9', 'Pfdn1', 'Zfp606', 'Dedd2', 'Xab2', 'Rabl3', 'Polr2g', 'Sec14l2', 'Sncaip', 'Nudt12', 'Gtf2e2', 'Zdhhc21', 'Sirt5', 'Phf5a', 'Ankra2', 'Gtf2f2', 'Tulp4', 'Zfp580', 'Cnot8', 'Ppp1r12b', 'Zfp219', 'Polr2i', '2810021J22Rik', 'Dpf3', 'Basp1', 'Polr3b', 'Arhgap17', 'Bbx', 'Zcchc8', 'Prdm16', 'Prdm5', 'Zdhhc1', 'Zfp597', 'Fmnl2', 'Pogk', 'Ing3', 'Phf7', 'Ddx54', 'Phf10', 'Asb6', 'Zfp131', 'Ssbp3', 'Bclaf1', 'Zfp248', 'Wasl', 'Hbp1', 'Rnf38', 'Btbd11', 'Zdhhc16', 'Gtf2e1', 'Cxxc1', 'Zfp84', 'Prr3', 'Sirt4', '1700003F12Rik', 'Acyp2', 'Rai14', 'Asb5', 'Tbx18', 'Ssbp4', 'Zfp142', 'Lbh', '4930522L14Rik', 'Helz', 'Asb8', 'Brd8', 'Brpf1', 'Sp2', '9130019O22Rik', 'Polr3h', 'Zfp319', 'Abtb1', 'Mynn', 'Sp6', 'Gtf2a1', 'Sirt1', 'Maged1', 'Ptges2', 'Zfp451', 'Taf13', 'Zfp282', 'Taf5l', 'Snapc2', 'Ankrd10', 'Rcor1', 'Bmyc', 'Chd4', 'Zdhhc15', 'Ankhd1', 'Uhrf2', 'Lmo1', 'Ank2', 'Zfp91', 'Dek', 'Dbx2', 'Brdt', 'Pawr', 'Baz2a', 'Mta3', 'Ivns1abp', 'Ankrd6', 'Ubn1', 'Zfp287', 'Zfp617', 'Ash1l', 'Clp1', 'Ell2', 'Dot1l', 'Hmgcs1', 'Sirt7', 'Zfp180', 'Myocd', 'L3mbtl2', 'Polrmt', 'Ugp2', 'Btf3', 'Zfp523', 'Trerf1', 'Zfp281', 'Pms1', 'Zdhhc5', 'Ebf4', 'Nkx2-4', 'Asxl1', 'Tgif2', 'Zfp334', 'Pbxip1', 'Ddx58', 'Zfp189', 'Glis1', 'Tardbp', 'Phf13', 'Hdac11', 'Ppp1r12c', 'Zfp592', 'Zfp553', 'Zfp612', 'Zfp426', 'Rab8b', 'Scap', 'Tceal1', 'Zfp454', 'Homez', 'Fem1c', 'Setbp1', 'Carf', 'Polr2h', 'Zfp277', 'Shprh', 'Phf12', 'Zbtb1', 'Caskin1', 'Gcdh', 'Zfp398', 'A630089N07Rik', 'Phf8', 'Zhx3', 'Zfp82']
['Arid3b', 'Arid3c', 'Bcl3', 'Barx2', 'Cited1', 'Cited4', 'E2f2', 'Elf4', 'Fosb', 'Fezf2', 'Fli1', 'Gsx2', 'Hmx2', 'Ikzf3', 'Jdp2', 'Klf1', 'Lhx1', 'Lhx3', 'Lmx1b', 'Nkx6-1', 'Nanog', 'Pou4f1', 'Prdm1', 'Rcor2', 'Spdef', 'Sertad1', 'Sox17', 'Sox7', 'Sp7', 'Tal1', 'Tbx20', 'Tbx3', 'Tbx6', 'Taf7l', 'Tead4', 'Tgif1', 'Txk', 'Atf3', 'Atoh7', 'Atoh8', 'Batf', 'Creb3l3', 'Creb3l4', 'Ciita', 'Crxos', 'Dlx5', 'Egr2', 'Egr3', 'Esr2', 'Esrrb', 'Esx1', 'Foxd1', 'Foxp3', 'Foxp4', 'Fosl2', 'Gbx2', 'Gadd45a', 'Hsf4', 'Hmga1', 'Hesx1', 'Irf8', 'Mn1', 'Msx1', 'Msc', 'Myb', 'Mybl1', 'Mybl2', 'Myc', 'Mllt11', 'Myog', 'Nrl', 'Neurod1', 'Npas1', 'Nfatc2', 'Nfatc4', 'Nfkb2', 'Nfe2l3', 'Nrip3', 'Nr4a3', 'Nr5a2', 'Onecut1', 'Onecut2', 'Onecut3', 'Ovol2', 'Pax1', 'Pax5', 'Pax7', 'Pax8', 'Prrx2', 'Pparg', 'Plagl1', 'Pcgf2', 'Pgr', 'Pcbd1', 'Rbpjl', 'Rfx1', 'Runx1', 'Scml2', 'Stat4', 'Stat6', 'Six4', 'Sim2', 'Snai1', 'Snai2', 'Satb2', 'Tcea2', 'Tcf7', 'Tfap4', 'Tfec', 'Tada3', 'Tgfb1i1', 'Trim25', 'Uaca', 'Mafa', 'Maff', 'Mafk', 'Mycn', 'Vav1', 'Vsx2', 'Vdr', 'Zfy1', 'Zfp128', 'Zfp354a', 'Zfp472', 'Zfp90', 'Zic2', 'Zmynd10', 'Aebp1', 'Ahrr', 'Neurog2', 'Bard1', 'Bcl6b', 'Brca1', 'Camk4', 'Cbx2', 'Cdkn2b', 'Cdkn2c', 'Dlx1', 'Ebf2', 'Lhx5', 'Ascl2', 'Mcm3', 'Mcm2', 'Mcm5', 'Mpl', 'Uhrf1', 'Musk', 'Pou2f3', 'Rorc', 'Six6', 'Tcea3', 'Tnfaip3', 'Zfp39', 'Zfp54', 'Zfp57', 'Zfp59', 'Zfp94', 'Zfp97', 'Zfy2', 'Zim1', 'Zfp354b', 'Hnf4g', 'Sirt6', 'Dmrt1', 'Htatip2', 'Pdlim1', 'Zfp108', 'Ruvbl1', 'Insm2', 'Fhl5', 'Shank3', 'Nrip2', 'Asb4', 'Fank1', '4930430A15Rik', 'Asb11', 'Trip13', 'Asb12', '4931423N10Rik', 'Fbxo24', '4933429H19Rik', 'Lsm11', 'Nkd2', 'Nptxr', 'Xrcc3', 'Hsf2bp', 'Zbtb3', 'Snip1', '2410141K09Rik', 'Asb15', 'Zfp202', 'Dach2', 'Rab15', 'Tcf19', 'Lmx1a', 'Foxn4', 'Rem2', '6720489N17Rik', 'Zswim4', 'Gls2', 'Taf5', 'Ppp1r16b', 'Gbx1', 'Creb5', 'BC024063', 'St18', 'Dmrta1', 'Dmrta2', 'Obox3', 'Gtf3c4', 'BC024139']

for gene in tfcols:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        vmax="p90.0",
        na_color="#9A9FB080",
        save="-tf1-" + gene + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf1-Rora-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf1-Npas3-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

for gene in tfcols2:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        vmin=0.0,
        na_color="#9A9FB080",
        save="-tf2" + gene + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf2Aff3-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf2Arid1a-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf2Arid1b-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf2Arid2-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

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WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf2Arid4b-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

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WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf2Ctbp1-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

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WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf2C1d-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

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for gene in tfcols3:
    sc.pl.umap(
        adata_eval2,
        color=gene,
        frameon=False,
        show=False,
        size=40,
        cmap=matplotlib.colors.LinearSegmentedColormap.from_list("YlBk", ["yellow", "black"], N=2),
        vmin=0.0,
        na_color="#9A9FB080",
        save="-tf3" + gene + save_PRJNA438862,
)

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WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Zfp202-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Dach2-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Rab15-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Tcf19-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Lmx1a-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Foxn4-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Rem2-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf36720489N17Rik-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Zswim4-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Gls2-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Taf5-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Ppp1r16b-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Gbx1-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Creb5-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3BC024063-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3St18-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Dmrta1-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Dmrta2-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Obox3-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3Gtf3c4-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/umap-tf3BC024139-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-npr-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-nmr-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-adgen-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_logit",
    color=tfcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-tf" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_logit-tf-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=nprcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-npr" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-npr-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=nmrcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-nmr" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-nmr-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=agcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-adgen" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-adgen-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

sc.pl.embedding(
    adata_eval2,
    basis="X_umap_subfunct",
    color=tfcols,
    frameon=False,
    show=True,
    size=40,
    vmin=0.0,
    vmax="p98.5",
    na_color="#9A9FB080",
    save="-tf" + save_PRJNA438862,
)

WARNING: saving figure to file output/figures/resolved_subregions_by_microclusters/X_umap_subfunct-tf-best_xgboost-subregional_supervised-PRJNA438862-astrocytes_datasets-msp_min_tree-metric_euclidean-k_10-sign_100-amb_0.001.pdf

Save Differential Expression Results

PRJNA779749_regulons.write(
    f"/data/data/resolved_subregions_by_microclusters/PRJNA779749_regulons-DER-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)

PRJNA548917_regulons.write(
    f"/data/data/resolved_subregions_by_microclusters/PRJNA548917_regulons-DER-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)

PRJNA547712_regulons.write(
    f"/data/data/resolved_subregions_by_microclusters/PRJNA547712_regulons-DER-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)

PRJNA438862_regulons.write(
    f"/data/data/resolved_subregions_by_microclusters/PRJNA438862_regulons-DER-astrocytes_dataset-msp_{connectivity_model}-metric_{metric}-k_{k}-sign_{signature}-amb_{cb_fpr}.h5ad"
)

Session info:

print(watermark())
print(watermark(iversions=True, globals_=globals()))

Last updated: 2023-05-17T04:50:34.256026+00:00

Python implementation: CPython
Python version       : 3.8.8
IPython version      : 8.12.1

Compiler    : GCC 9.3.0
OS          : Linux
Release     : 5.15.0-71-generic
Machine     : x86_64
Processor   : x86_64
CPU cores   : 256
Architecture: 64bit

joblib    : 1.2.0
matplotlib: 3.7.1
numpy     : 1.23.5
igraph    : 0.9.9
pandas    : 1.5.3
scanpy    : 1.9.3
umap      : 0.5.3
scipy     : 1.10.1