Signalling analysis of Insr positive vs. Insr negative astrocytes

Load data and setup parameters

Code

# Load tidyverse infrastructure packages
suppressPackageStartupMessages({
  library(reticulate)
  library(future)
  library(here)
  library(tidyverse)
  library(magrittr)
  library(stringr)
  library(skimr)
  library(RColorBrewer)
})


# Load packages for scRNA-seq analysis and visualisation
suppressPackageStartupMessages({
  library(ggplot2)
  library(ggrepel)
  library(cowplot)
  library(patchwork)
  library(ggstatsplot)
  library(ggupset)
  library(ComplexUpset)
  library(dabestr)
})

Set paths

Code

src_dir <- here("code")
data_dir <- here("data")
output_dir <- here("output")
plots_dir <- here(output_dir, "figures")
tables_dir <- here(output_dir, "tables")

Load helper functions and gene-sets

Code

source(here(src_dir, "genes.R"))
source(here(src_dir, "functions.R"))

Set fixed variables

Code

# set seed
reseed <- 42
set.seed(seed = reseed)

# Parameters for parallel execution
n_cores <- 8
plan("multisession", workers = n_cores)
options(
  future.globals.maxSize = 100000 * 1024^2,
  future.rng.onMisuse = "ignore"
)
plan()

multisession:
- args: function (..., workers = 8, envir = parent.frame())
- tweaked: TRUE
- call: plan("multisession", workers = n_cores)

Code

# ggplot2 theme
theme_set(ggmin::theme_powerpoint())

Load selected astrocytes signalling data estimated using Liana-py and Rank Aggregation method (including CellPhoneDB, CellChat, ICELLNET, connectomeDB2020, and CellTalkDB)

Code

lutomska_norm <-
  read_csv(here(
    data_dir,
    "signalling", "lutomska",
    "arc_astro-with_npy_pomc-liana-res.csv"
  )
)

lutomska_hfhsd <-
  read_csv(here(
    data_dir,
    "signalling", "lutomska",
    "hfd-arc_astro-with_npy_pomc-liana-res.csv"
  )
)

deng_norm <-
  read_csv(here(
    data_dir,
    "signalling", "deng",
    "arc_astro-with_npy_pomc-liana-res.csv"
  )
)

deng_hfd <-
  read_csv(here(
    data_dir,
    "signalling", "deng",
    "hfd-arc_astro-with_npy_pomc-liana-res.csv"
  )
)

lopez_norm <-
  read_csv(here(
    data_dir,
    "signalling", "lopez",
    "pvn_astro-with_npy_pomc-liana-res.csv"
  )
)

lopez_csds <-
  read_csv(here(
    data_dir,
    "signalling", "lopez",
    "chronic-stress-pvn_astro-with_npy_pomc-liana-res.csv"
  )
)

Code

skim(lutomska_norm)

Data summary

Name	lutomska_norm
Number of rows	40496
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	11	16	0	4	0
target	0	1	11	16	0	4	0
ligand_complex	0	1	1	16	0	572	0
receptor_complex	0	1	2	19	0	627	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	20247.50	11690.33	0.00	10123.75	20247.50	30371.25	40495.00	▇▇▇▇▇
lr_means	0	1	0.20	0.34	0.00	0.00	0.06	0.27	3.69	▇▁▁▁▁
cellphone_pvals	0	1	0.64	0.43	0.00	0.09	0.98	1.00	1.00	▅▁▁▁▇
expr_prod	0	1	0.08	0.44	0.00	0.00	0.00	0.02	13.31	▇▁▁▁▁
scaled_weight	0	1	0.10	0.40	-1.89	-0.07	0.01	0.19	4.34	▁▇▁▁▁
lr_logfc	0	1	-0.06	0.40	-3.98	-0.12	-0.01	0.06	3.74	▁▁▇▁▁
spec_weight	0	1	0.06	0.11	0.00	0.00	0.01	0.09	1.00	▇▁▁▁▁
lrscore	0	1	0.25	0.27	0.00	0.00	0.17	0.46	0.96	▇▃▂▂▁
lr_probs	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.32	▇▁▁▁▁
cellchat_pvals	0	1	0.92	0.26	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇
steady_rank	0	1	0.76	0.38	0.00	0.51	1.00	1.00	1.00	▂▁▁▁▇
specificity_rank	0	1	0.76	0.39	0.00	0.47	1.00	1.00	1.00	▂▁▁▁▇
magnitude_rank	0	1	0.88	0.29	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇

Code

skim(lutomska_hfhsd)

Data summary

Name	lutomska_hfhsd
Number of rows	45104
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	11	16	0	4	0
target	0	1	11	16	0	4	0
ligand_complex	0	1	1	16	0	627	0
receptor_complex	0	1	2	19	0	684	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	22551.50	13020.55	0.00	11275.75	22551.50	33827.25	45103.00	▇▇▇▇▇
lr_means	0	1	0.19	0.33	0.00	0.00	0.05	0.26	3.71	▇▁▁▁▁
cellphone_pvals	0	1	0.63	0.45	0.00	0.02	1.00	1.00	1.00	▅▁▁▁▇
expr_prod	0	1	0.07	0.39	0.00	0.00	0.00	0.01	13.41	▇▁▁▁▁
scaled_weight	0	1	0.09	0.42	-2.02	-0.07	0.01	0.19	4.68	▁▇▁▁▁
lr_logfc	0	1	-0.06	0.42	-4.21	-0.13	0.00	0.06	4.15	▁▁▇▁▁
spec_weight	0	1	0.06	0.11	0.00	0.00	0.01	0.09	1.00	▇▁▁▁▁
lrscore	0	1	0.24	0.27	0.00	0.00	0.14	0.42	0.96	▇▂▂▂▁
lr_probs	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.33	▇▁▁▁▁
cellchat_pvals	0	1	0.93	0.25	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇
steady_rank	0	1	0.76	0.38	0.00	0.51	1.00	1.00	1.00	▂▁▁▁▇
specificity_rank	0	1	0.76	0.38	0.00	0.44	1.00	1.00	1.00	▂▁▁▁▇
magnitude_rank	0	1	0.89	0.29	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇

Code

skim(deng_norm)

Data summary

Name	deng_norm
Number of rows	44128
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	11	16	0	4	0
target	0	1	11	16	0	4	0
ligand_complex	0	1	1	16	0	612	0
receptor_complex	0	1	2	19	0	682	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	22063.50	12738.80	0.00	11031.75	22063.50	33095.25	44127.00	▇▇▇▇▇
lr_means	0	1	0.22	0.36	0.00	0.00	0.09	0.29	3.74	▇▁▁▁▁
cellphone_pvals	0	1	0.60	0.43	0.00	0.07	0.86	1.00	1.00	▅▁▁▁▇
expr_prod	0	1	0.09	0.47	0.00	0.00	0.00	0.02	13.45	▇▁▁▁▁
scaled_weight	0	1	0.07	0.29	-1.42	-0.08	0.02	0.18	2.55	▁▇▃▁▁
lr_logfc	0	1	-0.04	0.40	-3.86	-0.08	0.00	0.06	5.31	▁▂▇▁▁
spec_weight	0	1	0.06	0.09	0.00	0.00	0.03	0.09	1.00	▇▁▁▁▁
lrscore	0	1	0.27	0.26	0.00	0.00	0.22	0.46	0.95	▇▃▂▂▁
lr_probs	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.32	▇▁▁▁▁
cellchat_pvals	0	1	0.90	0.29	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇
steady_rank	0	1	0.76	0.39	0.00	0.48	1.00	1.00	1.00	▂▁▁▁▇
specificity_rank	0	1	0.74	0.39	0.00	0.41	1.00	1.00	1.00	▂▁▁▁▇
magnitude_rank	0	1	0.86	0.31	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇

Code

skim(deng_hfd)

Data summary

Name	deng_hfd
Number of rows	45408
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	11	16	0	4	0
target	0	1	11	16	0	4	0
ligand_complex	0	1	1	16	0	611	0
receptor_complex	0	1	2	19	0	697	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	22703.50	13108.30	0.00	11351.75	22703.50	34055.25	45407.00	▇▇▇▇▇
lr_means	0	1	0.22	0.35	0.00	0.00	0.08	0.27	3.75	▇▁▁▁▁
cellphone_pvals	0	1	0.62	0.42	0.00	0.13	0.85	1.00	1.00	▅▁▁▁▇
expr_prod	0	1	0.09	0.48	0.00	0.00	0.00	0.02	13.79	▇▁▁▁▁
scaled_weight	0	1	0.08	0.32	-1.31	-0.06	0.01	0.16	3.53	▁▇▁▁▁
lr_logfc	0	1	-0.03	0.32	-2.51	-0.09	0.00	0.05	3.63	▁▃▇▁▁
spec_weight	0	1	0.06	0.10	0.00	0.00	0.03	0.09	1.00	▇▁▁▁▁
lrscore	0	1	0.26	0.26	0.00	0.00	0.19	0.45	0.96	▇▃▂▂▁
lr_probs	0	1	0.00	0.02	0.00	0.00	0.00	0.00	0.39	▇▁▁▁▁
cellchat_pvals	0	1	0.91	0.28	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇
steady_rank	0	1	0.77	0.38	0.00	0.52	1.00	1.00	1.00	▂▁▁▁▇
specificity_rank	0	1	0.75	0.38	0.00	0.45	1.00	1.00	1.00	▂▁▁▁▇
magnitude_rank	0	1	0.87	0.31	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇

Code

skim(lopez_norm)

Data summary

Name	lopez_norm
Number of rows	102600
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	10	16	0	6	0
target	0	1	10	16	0	6	0
ligand_complex	0	1	1	16	0	629	0
receptor_complex	0	1	2	19	0	694	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	51299.50	29618.21	0.00	25649.75	51299.50	76949.25	102599.00	▇▇▇▇▇
lr_means	0	1	0.18	0.34	0.00	0.00	0.04	0.24	3.74	▇▁▁▁▁
cellphone_pvals	0	1	0.61	0.44	0.00	0.06	0.96	1.00	1.00	▅▁▁▁▇
expr_prod	0	1	0.07	0.43	0.00	0.00	0.00	0.01	13.45	▇▁▁▁▁
scaled_weight	0	1	0.15	0.38	-1.69	-0.04	0.05	0.23	4.55	▁▇▁▁▁
lr_logfc	0	1	-0.03	0.39	-3.65	-0.09	0.00	0.07	6.05	▁▇▁▁▁
spec_weight	0	1	0.03	0.06	0.00	0.00	0.01	0.03	1.00	▇▁▁▁▁
lrscore	0	1	0.26	0.29	0.00	0.00	0.15	0.48	0.97	▇▂▂▂▁
lr_probs	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.31	▇▁▁▁▁
cellchat_pvals	0	1	0.93	0.25	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇
steady_rank	0	1	0.77	0.38	0.00	0.57	1.00	1.00	1.00	▂▁▁▁▇
specificity_rank	0	1	0.77	0.38	0.00	0.53	1.00	1.00	1.00	▂▁▁▁▇
magnitude_rank	0	1	0.89	0.29	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇

Code

skim(lopez_csds)

Data summary

Name	lopez_csds
Number of rows	106524
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	10	16	0	6	0
target	0	1	10	16	0	6	0
ligand_complex	0	1	1	16	0	640	0
receptor_complex	0	1	2	19	0	714	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	53261.50	30750.97	0.00	26630.75	53261.50	79892.25	106523.00	▇▇▇▇▇
lr_means	0	1	0.18	0.34	0.00	0.00	0.02	0.22	3.75	▇▁▁▁▁
cellphone_pvals	0	1	0.64	0.43	0.00	0.11	1.00	1.00	1.00	▃▁▁▁▇
expr_prod	0	1	0.07	0.43	0.00	0.00	0.00	0.01	13.78	▇▁▁▁▁
scaled_weight	0	1	0.13	0.40	-1.51	-0.05	0.03	0.17	5.86	▃▇▁▁▁
lr_logfc	0	1	-0.02	0.34	-3.19	-0.09	0.00	0.06	4.05	▁▁▇▁▁
spec_weight	0	1	0.03	0.06	0.00	0.00	0.00	0.03	1.00	▇▁▁▁▁
lrscore	0	1	0.23	0.28	0.00	0.00	0.09	0.44	0.97	▇▂▂▂▁
lr_probs	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.31	▇▁▁▁▁
cellchat_pvals	0	1	0.93	0.24	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇
steady_rank	0	1	0.78	0.37	0.00	0.62	1.00	1.00	1.00	▂▁▁▁▇
specificity_rank	0	1	0.78	0.37	0.00	0.59	1.00	1.00	1.00	▂▁▁▁▇
magnitude_rank	0	1	0.89	0.28	0.00	1.00	1.00	1.00	1.00	▁▁▁▁▇

Filter by p-values

Code

lutomska_norm <-
  lutomska_norm |>
  filter(
    specificity_rank <= 0.05,
    magnitude_rank <= 0.05
  )

lutomska_hfhsd <-
  lutomska_hfhsd |>
  filter(
    specificity_rank <= 0.05,
    magnitude_rank <= 0.05
  )

deng_norm <-
  deng_norm |>
  filter(
    specificity_rank <= 0.05,
    magnitude_rank <= 0.05
  )

deng_hfd <-
  deng_hfd |>
  filter(
    specificity_rank <= 0.05,
    magnitude_rank <= 0.05
  )

lopez_norm <-
  lopez_norm |>
  filter(
    specificity_rank <= 0.05,
    magnitude_rank <= 0.05
  )

lopez_csds <-
  lopez_csds |>
  filter(
    specificity_rank <= 0.05,
    magnitude_rank <= 0.05
  )

Code

skim(lutomska_norm)

Data summary

Name	lutomska_norm
Number of rows	1641
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	11	16	0	4	0
target	0	1	11	16	0	4	0
ligand_complex	0	1	2	8	0	164	0
receptor_complex	0	1	2	13	0	194	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	18103.21	11035.58	20.00	7901.00	18566.00	27036.00	40473.00	▇▆▆▇▃
lr_means	0	1	1.01	0.54	0.45	0.61	0.85	1.26	3.69	▇▃▁▁▁
cellphone_pvals	0	1	0.01	0.03	0.00	0.00	0.00	0.00	0.93	▇▁▁▁▁
expr_prod	0	1	0.97	1.32	0.10	0.29	0.55	1.06	13.31	▇▁▁▁▁
scaled_weight	0	1	0.71	0.68	0.05	0.22	0.46	0.99	4.34	▇▂▁▁▁
lr_logfc	0	1	0.44	0.48	-0.07	0.15	0.28	0.58	3.74	▇▂▁▁▁
spec_weight	0	1	0.16	0.08	0.02	0.10	0.14	0.18	0.95	▇▂▁▁▁
lrscore	0	1	0.83	0.06	0.67	0.78	0.83	0.87	0.96	▁▆▇▇▃
lr_probs	0	1	0.03	0.04	0.00	0.01	0.01	0.03	0.32	▇▁▁▁▁
cellchat_pvals	0	1	0.03	0.15	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
steady_rank	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
specificity_rank	0	1	0.01	0.01	0.00	0.00	0.00	0.01	0.05	▇▁▁▁▁
magnitude_rank	0	1	0.01	0.01	0.00	0.00	0.01	0.02	0.05	▇▂▂▁▁

Code

skim(lutomska_hfhsd)

Data summary

Name	lutomska_hfhsd
Number of rows	1558
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	11	16	0	4	0
target	0	1	11	16	0	4	0
ligand_complex	0	1	2	8	0	172	0
receptor_complex	0	1	2	13	0	192	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	20202.67	12167.60	79.00	8826.00	20827.00	29949.75	45074.00	▇▆▆▇▃
lr_means	0	1	1.01	0.53	0.44	0.60	0.86	1.24	3.71	▇▂▁▁▁
cellphone_pvals	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.14	▇▁▁▁▁
expr_prod	0	1	0.95	1.29	0.10	0.29	0.53	1.10	13.41	▇▁▁▁▁
scaled_weight	0	1	0.73	0.74	0.02	0.22	0.44	0.98	4.68	▇▂▁▁▁
lr_logfc	0	1	0.46	0.52	-0.03	0.15	0.29	0.59	4.15	▇▁▁▁▁
spec_weight	0	1	0.16	0.09	0.03	0.10	0.14	0.19	0.90	▇▂▁▁▁
lrscore	0	1	0.84	0.06	0.69	0.79	0.84	0.88	0.96	▂▆▇▇▂
lr_probs	0	1	0.03	0.04	0.00	0.01	0.01	0.03	0.33	▇▁▁▁▁
cellchat_pvals	0	1	0.03	0.15	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
steady_rank	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
specificity_rank	0	1	0.01	0.01	0.00	0.00	0.00	0.01	0.05	▇▁▁▁▁
magnitude_rank	0	1	0.01	0.01	0.00	0.00	0.00	0.02	0.05	▇▂▁▁▁

Code

skim(deng_norm)

Data summary

Name	deng_norm
Number of rows	1796
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	11	16	0	4	0
target	0	1	11	16	0	4	0
ligand_complex	0	1	2	16	0	187	0
receptor_complex	0	1	2	13	0	212	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	19375.81	12096.10	22.00	9536.00	18041.00	28294.75	44009.00	▇▇▆▅▅
lr_means	0	1	1.11	0.61	0.47	0.63	0.92	1.39	3.74	▇▃▂▁▁
cellphone_pvals	0	1	0.01	0.03	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
expr_prod	0	1	1.04	1.51	0.01	0.28	0.53	1.22	13.45	▇▁▁▁▁
scaled_weight	0	1	0.55	0.40	0.00	0.23	0.44	0.80	2.55	▇▅▂▁▁
lr_logfc	0	1	0.53	0.72	-1.08	0.19	0.32	0.58	5.31	▃▇▁▁▁
spec_weight	0	1	0.14	0.08	0.02	0.09	0.12	0.17	0.81	▇▂▁▁▁
lrscore	0	1	0.80	0.07	0.39	0.75	0.81	0.86	0.95	▁▁▂▇▅
lr_probs	0	1	0.03	0.04	0.00	0.01	0.01	0.04	0.32	▇▁▁▁▁
cellchat_pvals	0	1	0.06	0.22	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
steady_rank	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
specificity_rank	0	1	0.01	0.01	0.00	0.00	0.00	0.01	0.05	▇▁▁▁▁
magnitude_rank	0	1	0.01	0.01	0.00	0.00	0.01	0.02	0.05	▇▂▂▂▁

Code

skim(deng_hfd)

Data summary

Name	deng_hfd
Number of rows	1750
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	11	16	0	4	0
target	0	1	11	16	0	4	0
ligand_complex	0	1	2	8	0	166	0
receptor_complex	0	1	2	13	0	193	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	19761.75	12493.86	20.00	8853.25	18523.50	29188.25	45383.00	▇▇▆▆▅
lr_means	0	1	1.13	0.57	0.48	0.69	0.98	1.43	3.75	▇▃▂▁▁
cellphone_pvals	0	1	0.02	0.05	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
expr_prod	0	1	1.17	1.62	0.09	0.33	0.61	1.38	13.79	▇▁▁▁▁
scaled_weight	0	1	0.60	0.47	0.05	0.24	0.51	0.82	3.53	▇▃▁▁▁
lr_logfc	0	1	0.45	0.43	-0.49	0.16	0.30	0.64	3.63	▇▆▁▁▁
spec_weight	0	1	0.13	0.06	0.02	0.09	0.12	0.16	0.61	▇▃▁▁▁
lrscore	0	1	0.82	0.07	0.63	0.77	0.82	0.87	0.96	▁▅▇▇▃
lr_probs	0	1	0.04	0.06	0.00	0.01	0.02	0.06	0.39	▇▁▁▁▁
cellchat_pvals	0	1	0.04	0.14	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
steady_rank	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
specificity_rank	0	1	0.01	0.01	0.00	0.00	0.00	0.01	0.05	▇▁▁▁▁
magnitude_rank	0	1	0.01	0.01	0.00	0.00	0.00	0.02	0.05	▇▂▁▁▁

Code

skim(lopez_norm)

Data summary

Name	lopez_norm
Number of rows	3441
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	10	16	0	6	0
target	0	1	10	16	0	6	0
ligand_complex	0	1	2	16	0	205	0
receptor_complex	0	1	2	13	0	230	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	53797.03	32923.79	21.00	18237.00	60858.00	83786.00	102596.00	▇▃▃▆▇
lr_means	0	1	1.08	0.63	0.42	0.60	0.88	1.37	3.74	▇▃▁▁▁
cellphone_pvals	0	1	0.01	0.05	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
expr_prod	0	1	1.04	1.51	0.07	0.28	0.52	1.19	13.45	▇▁▁▁▁
scaled_weight	0	1	0.82	0.66	0.05	0.34	0.57	1.18	4.55	▇▃▁▁▁
lr_logfc	0	1	0.52	0.58	-1.01	0.17	0.35	0.68	6.05	▇▆▁▁▁
spec_weight	0	1	0.07	0.06	0.01	0.04	0.06	0.09	0.58	▇▁▁▁▁
lrscore	0	1	0.85	0.06	0.67	0.81	0.85	0.90	0.97	▁▃▇▇▅
lr_probs	0	1	0.03	0.04	0.00	0.00	0.01	0.03	0.31	▇▁▁▁▁
cellchat_pvals	0	1	0.05	0.21	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
steady_rank	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
specificity_rank	0	1	0.01	0.01	0.00	0.00	0.00	0.01	0.05	▇▁▁▁▁
magnitude_rank	0	1	0.01	0.01	0.00	0.00	0.00	0.02	0.05	▇▂▁▁▁

Code

skim(lopez_csds)

Data summary

Name	lopez_csds
Number of rows	3476
Number of columns	17
_______________________
Column type frequency:
character	4
numeric	13
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	10	16	0	6	0
target	0	1	10	16	0	6	0
ligand_complex	0	1	2	10	0	193	0
receptor_complex	0	1	2	13	0	214	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
…1	0	1	57214.53	33490.74	81.00	21402.00	64002.50	86963.75	106518.00	▇▃▅▇▇
lr_means	0	1	1.11	0.62	0.43	0.64	0.93	1.41	3.75	▇▃▁▁▁
cellphone_pvals	0	1	0.01	0.04	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
expr_prod	0	1	1.12	1.54	0.05	0.31	0.57	1.25	13.78	▇▁▁▁▁
scaled_weight	0	1	0.82	0.72	0.05	0.30	0.54	1.17	5.86	▇▂▁▁▁
lr_logfc	0	1	0.52	0.48	-0.33	0.17	0.37	0.73	4.05	▇▃▁▁▁
spec_weight	0	1	0.07	0.05	0.01	0.04	0.06	0.08	0.59	▇▁▁▁▁
lrscore	0	1	0.85	0.06	0.63	0.80	0.85	0.89	0.97	▁▂▆▇▅
lr_probs	0	1	0.03	0.04	0.00	0.00	0.01	0.03	0.31	▇▁▁▁▁
cellchat_pvals	0	1	0.05	0.21	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
steady_rank	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.04	▇▁▁▁▁
specificity_rank	0	1	0.01	0.01	0.00	0.00	0.00	0.01	0.05	▇▁▁▁▁
magnitude_rank	0	1	0.01	0.01	0.00	0.00	0.00	0.01	0.05	▇▂▁▁▁

Merge datasets

Code

lutomska_norm <-
  lutomska_norm |>
  mutate(
    region = "ARC",
    condition = "HFHSD",
    control = TRUE
  )

lutomska_hfhsd <-
  lutomska_hfhsd |>
  mutate(
    region = "ARC",
    condition = "HFHSD",
    control = FALSE
  )

deng_norm <-
  deng_norm |>
  mutate(
    region = "ARC",
    condition = "HFD",
    control = TRUE
  )

deng_hfd <-
  deng_hfd |>
  mutate(
    region = "ARC",
    condition = "HFD",
    control = FALSE
  )

lopez_norm <-
  lopez_norm |>
  mutate(
    region = "PVN",
    condition = "CSDS",
    control = TRUE
  )

lopez_csds <-
  lopez_csds |>
  mutate(
    region = "PVN",
    condition = "CSDS",
    control = FALSE
  )

df_complex_conditions <-
  bind_rows(
    lutomska_norm,
    lutomska_hfhsd,
    deng_norm,
    deng_hfd,
    lopez_norm,
    lopez_csds
  )
df_complex_conditions <-
  df_complex_conditions |>
  mutate(pairs = str_c(source, target, sep = "==>")) |>
  filter(pairs %in% c(
    "Astro_INSR+==>POMC neurons",
    "Astro_INSR-==>POMC neurons",
    "Astro_INSR+==>AGRP/NPY neurons",
    "Astro_INSR-==>AGRP/NPY neurons",
    "POMC neurons==>Astro_INSR+",
    "AGRP/NPY neurons==>Astro_INSR+",
    "POMC neurons==>Astro_INSR-",
    "AGRP/NPY neurons==>Astro_INSR-"
  ))

Intersection sets analysis

Code

df_complex_conditions2 <- df_complex_conditions %>%
  mutate(Label = pmap(list(region, condition, control, pairs), function(region, condition, control, pairs) {
    c(
      str_c(region, condition, sep = "_"),
      if (control) {
        "Control"
      },
      pairs
    )
  }))

df_complex_conditions2 %>%
  ggplot(aes(x = Label)) +
  geom_bar() +
  scale_x_upset(
    order_by = "degree",
    sets = c(
      "ARC_HFHSD",
      "ARC_HFD",
      "PVN_CSDS",
      "Control",
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons",
      "POMC neurons==>Astro_INSR+",
      "AGRP/NPY neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-",
      "AGRP/NPY neurons==>Astro_INSR-"
    ),
    name = ""
  ) +
  theme_combmatrix(
    combmatrix.label.text = element_text(size = 12),
    combmatrix.label.extra_spacing = 5
  )

Code

df_complex_conditions2 |>
  filter(pairs %in% c(
    "Astro_INSR+==>POMC neurons",
    "Astro_INSR-==>POMC neurons",
    "Astro_INSR+==>AGRP/NPY neurons",
    "Astro_INSR-==>AGRP/NPY neurons"
  )) %>%
  ggplot(aes(x = Label, y = -log10(magnitude_rank))) +
  geom_boxplot() +
  geom_jitter(aes(color = region), width = 0.1) +
  scale_x_upset(
    order_by = "degree",
    sets = c(
      "ARC_HFHSD",
      "ARC_HFD",
      "PVN_CSDS",
      "Control",
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons"
      # "POMC neurons==>Astro_INSR+",
      # "AGRP/NPY neurons==>Astro_INSR+",
      # "POMC neurons==>Astro_INSR-",
      # "AGRP/NPY neurons==>Astro_INSR-"
    ),
    position = "top",
    name = ""
  ) +
  theme_combmatrix(
    combmatrix.label.text = element_text(size = 12),
    combmatrix.label.extra_spacing = 5
  )

Code

df_details <- df_complex_conditions |>
    filter(pairs %in% c(
        "Astro_INSR+==>POMC neurons",
        "Astro_INSR-==>POMC neurons",
        "Astro_INSR+==>AGRP/NPY neurons",
        "Astro_INSR-==>AGRP/NPY neurons"
    )) |> mutate(filler = 1) |>
    pivot_wider(
        names_from = condition,
        values_from = filler,
        values_fill = 0) |>
    mutate(filler = 1) |>
    pivot_wider(
        names_from = pairs,
        values_from = filler,
        values_fill = 0) |>
    mutate(control = as.integer(control))

upset(
    df_details,
    c("HFHSD",
      "HFD",
      "CSDS",
      'control',
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons"),
    annotations = list(
        'Magnitude' = list(
            aes = aes(
                x = intersection,
                y = -log10(magnitude_rank
                )),
            geom = geom_boxplot(na.rm = TRUE)
        ),
        'LogFoldChange' = list(
            aes = aes(
                x = intersection,
                y = lr_logfc
            ),
            geom = list(
                geom_jitter(aes(color = -log10(specificity_rank)), na.rm = TRUE),
                geom_violin(alpha = 0.5, na.rm = TRUE)
            )
        )
    ),
    min_degree = 3,
    min_size = 10,
    width_ratio = 0.1,
    themes = modifyList(upset_themes,
                        list(
                            Magnitude = theme(), LogFoldChange = theme()
                        )),
    group_by = 'sets',
    queries = list(
        upset_query(
            group = "Astro_INSR+==>POMC neurons",
            color = 'blue'
        ),
        upset_query(
            group = "Astro_INSR-==>POMC neurons",
            color = 'orange'
        ),
        upset_query(
            group = "Astro_INSR+==>AGRP/NPY neurons",
            color = 'purple'
        ),
        upset_query(
            group = "Astro_INSR-==>AGRP/NPY neurons",
            color = 'cyan'
        ),
        upset_query(
            set = "Astro_INSR+==>POMC neurons",
            fill = 'blue'
        ),
        upset_query(
            set = "Astro_INSR-==>POMC neurons",
            fill = 'orange'
        ),
        upset_query(
            set = "Astro_INSR+==>AGRP/NPY neurons",
            fill = 'purple'
        ),
        upset_query(
            set = "Astro_INSR-==>AGRP/NPY neurons",
            fill = 'cyan'
        )),
    set_sizes = (
        upset_set_size(
            # geom=geom_bar(
            #     aes(fill=control, x=group),
            #     width=0.8
            # ),
            # position='right'
        )
        + geom_text(aes(label = ..count..), hjust = 1.1, stat = 'count')
        + theme(axis.text.x = element_text(angle = 90))
    ),
    stripes = upset_stripes(
        geom = geom_segment(size = 5),
        colors = c('cornsilk1', 'deepskyblue1')
    )
)

Code

df <- df_complex_conditions %>%
  filter(
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons"
    ),
    condition == "HFD"
  ) %>%
  mutate(
    neg_log10_specificity_rank = -log10(specificity_rank),
    neg_log10_magnitude_rank = -log10(magnitude_rank),
    lr_complex = str_c(ligand_complex, receptor_complex, sep = "–>")
  ) %>%
  select(pairs, lr_complex, ligand_complex, receptor_complex, neg_log10_specificity_rank, neg_log10_magnitude_rank, control)


top_bot_10 <- df %>%
  group_by(pairs, control) %>%
  slice_max(order_by = neg_log10_specificity_rank, n = 20)

Code

pr <- ggplot(df, aes(x = pairs, y = neg_log10_specificity_rank, color = as.factor(control), size = neg_log10_magnitude_rank)) +
  geom_jitter(width = 0.1) +
  geom_line(aes(group = lr_complex), linetype = "solid", alpha = 0.2) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  facet_wrap(~control)

# Add text labels for min and max 5 values within each group using ggrepel
pr <- pr + geom_text_repel(
  data = top_bot_10 %>% filter(pairs == "Astro_INSR+==>POMC neurons"),
  aes(
    label = receptor_complex,
    point.size = neg_log10_magnitude_rank
  ), # data point size
  nudge_x = 0.2, direction = "y", hjust = "left",
  size = 6, # font size in the text labels
  point.padding = 0, # additional padding around each point
  min.segment.length = 0, # draw all line segments
  max.time = 5, max.iter = 1e6, # stop after 1 second, or after 100,000 iterations
  seed = reseed,
  max.overlaps = Inf,
  box.padding = 0.5
) + geom_text_repel(
  data = top_bot_10 %>% filter(pairs == "Astro_INSR-==>POMC neurons"),
  aes(
    label = receptor_complex,
    point.size = neg_log10_magnitude_rank
  ), # data point size
  nudge_x = -.2, direction = "y", hjust = "right",
  size = 6, # font size in the text labels
  point.padding = 0, # additional padding around each point
  min.segment.length = 0, # draw all line segments
  max.time = 5, max.iter = 1e6, # stop after 1 second, or after 100,000 iterations
  seed = reseed,
  max.overlaps = Inf,
  box.padding = 0.5
)

print(pr)

Code

pl <- ggplot(df, aes(x = pairs, y = neg_log10_specificity_rank, color = as.factor(control), size = neg_log10_magnitude_rank)) +
  geom_jitter(width = 0.1) +
  geom_line(aes(group = lr_complex), linetype = "solid", alpha = 0.2) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  facet_wrap(~control)

# Add text labels for min and max 5 values within each group using ggrepel
pl <- pl + geom_text_repel(
  data = top_bot_10 %>% filter(pairs == "Astro_INSR+==>POMC neurons"),
  aes(
    label = ligand_complex,
    point.size = neg_log10_magnitude_rank
  ), # data point size
  nudge_x = 0.2, direction = "y", hjust = "left",
  size = 6, # font size in the text labels
  point.padding = 0, # additional padding around each point
  min.segment.length = 0, # draw all line segments
  max.time = 5, max.iter = 1e6, # stop after 1 second, or after 100,000 iterations
  seed = reseed,
  max.overlaps = Inf,
  box.padding = 0.5
) + geom_text_repel(
  data = top_bot_10 %>% filter(pairs == "Astro_INSR-==>POMC neurons"),
  aes(
    label = ligand_complex,
    point.size = neg_log10_magnitude_rank
  ), # data point size
  nudge_x = -.2, direction = "y", hjust = "right",
  size = 6, # font size in the text labels
  point.padding = 0, # additional padding around each point
  min.segment.length = 0, # draw all line segments
  max.time = 5, max.iter = 1e6, # stop after 1 second, or after 100,000 iterations
  seed = reseed,
  max.overlaps = Inf,
  box.padding = 0.5
)
print(pl)

Code

plr <- ggplot(df, aes(x = pairs, y = neg_log10_specificity_rank, color = as.factor(control), size = neg_log10_magnitude_rank)) +
  geom_jitter(width = 0.1) +
  geom_line(aes(group = lr_complex), linetype = "solid", alpha = 0.2) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  facet_wrap(~control)

# Add text labels for min and max 5 values within each group using ggrepel
plr <- plr + geom_text_repel(
  data = top_bot_10 %>% filter(pairs == "Astro_INSR+==>POMC neurons"),
  aes(
    label = lr_complex,
    point.size = neg_log10_magnitude_rank
  ), # data point size
  nudge_x = 0.2, direction = "y", hjust = "left",
  size = 6, # font size in the text labels
  point.padding = 0, # additional padding around each point
  min.segment.length = 0, # draw all line segments
  max.time = 5, max.iter = 1e6, # stop after 1 second, or after 100,000 iterations
  seed = reseed,
  max.overlaps = Inf,
  box.padding = 0.5
) + geom_text_repel(
  data = top_bot_10 %>% filter(pairs == "Astro_INSR-==>POMC neurons"),
  aes(
    label = lr_complex,
    point.size = neg_log10_magnitude_rank
  ), # data point size
  nudge_x = -.2, direction = "y", hjust = "right",
  size = 6, # font size in the text labels
  point.padding = 0, # additional padding around each point
  min.segment.length = 0, # draw all line segments
  max.time = 5, max.iter = 1e6, # stop after 1 second, or after 100,000 iterations
  seed = reseed,
  max.overlaps = Inf,
  box.padding = 0.5
)
print(plr)

Astrocytes >>> Neurons

Arcuate

Specificity

Code

df.two.group.unpaired <-
  df_complex_conditions |>
  select(
    `...1`,
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    region == "ARC",
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons"
    )
  ) |>
  mutate(neg_log10_specificity_rank = -log10(specificity_rank))

two.group.unpaired <-
  df.two.group.unpaired |>
  dabest(
    x = pairs,
    y = neg_log10_specificity_rank,
    idx = c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons"
    ),
    paired = FALSE,
    id.column = "...1"
  )

two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:39 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_specificity_rank

Unpaired mean difference of Astro_INSR-==>POMC neurons (n = 377) minus Astro_INSR+==>POMC neurons (n = 454)
 -0.222 [95CI  -0.37; -0.0832]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.meandiff,
  slopegraph = FALSE, color.column = control
)

Code

two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_d

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:39 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_specificity_rank

Unpaired Cohen's d of Astro_INSR-==>POMC neurons (n = 377) minus Astro_INSR+==>POMC neurons (n = 454)
 -0.213 [95CI  -0.356; -0.0781]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.cohens_d,
  slopegraph = FALSE, color.column = control
)

Multi-two group

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    region == "ARC",
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons"
    )
  ) |>
  mutate(
    neg_log10_specificity_rank = -log10(specificity_rank),
    control = if_else(
      control,
      "Norm",
      "HFD"
    )
  ) |>
  unite(
    control,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_specificity_rank,
    idx = list(
      c(
        "Norm: Astro_INSR+==>POMC neurons",
        "Norm: Astro_INSR-==>POMC neurons",
        "Norm: Astro_INSR+==>AGRP/NPY neurons",
        "Norm: Astro_INSR-==>AGRP/NPY neurons"
      ),
      c(
        "HFD: Astro_INSR+==>POMC neurons",
        "HFD: Astro_INSR-==>POMC neurons",
        "HFD: Astro_INSR+==>AGRP/NPY neurons",
        "HFD: Astro_INSR-==>AGRP/NPY neurons"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff, color.column = condition)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d, color.column = condition)

Magnitude

Code

df.two.group.unpaired <-
  df_complex_conditions |>
  select(
    `...1`,
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    region == "ARC",
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons"
    )
  ) |>
  mutate(neg_log10_magnitude_rank = -log10(magnitude_rank))

two.group.unpaired <-
  df.two.group.unpaired |>
  dabest(
    x = pairs,
    y = neg_log10_magnitude_rank,
    idx = c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons"
    ),
    paired = FALSE,
    id.column = "...1"
  )

two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:40 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_magnitude_rank

Unpaired mean difference of Astro_INSR-==>POMC neurons (n = 377) minus Astro_INSR+==>POMC neurons (n = 454)
 -0.0276 [95CI  -0.216; 0.154]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.meandiff,
  slopegraph = FALSE, color.column = control
)

Code

two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_d

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:40 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_magnitude_rank

Unpaired Cohen's d of Astro_INSR-==>POMC neurons (n = 377) minus Astro_INSR+==>POMC neurons (n = 454)
 -0.0205 [95CI  -0.157; 0.118]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.cohens_d,
  slopegraph = FALSE, color.column = control
)

Multi-two group

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    region == "ARC",
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons"
    )
  ) |>
  mutate(
    neg_log10_magnitude_rank = -log10(magnitude_rank),
    control = if_else(
      control,
      "Norm",
      "HFD"
    )
  ) |>
  unite(
    control,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_magnitude_rank,
    idx = list(
      c(
        "Norm: Astro_INSR+==>POMC neurons",
        "Norm: Astro_INSR-==>POMC neurons",
        "Norm: Astro_INSR+==>AGRP/NPY neurons",
        "Norm: Astro_INSR-==>AGRP/NPY neurons"
      ),
      c(
        "HFD: Astro_INSR+==>POMC neurons",
        "HFD: Astro_INSR-==>POMC neurons",
        "HFD: Astro_INSR+==>AGRP/NPY neurons",
        "HFD: Astro_INSR-==>AGRP/NPY neurons"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
  color.column = condition
)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
  color.column = condition
)

PVN

Specificity

Code

df.two.group.unpaired <-
  df_complex_conditions |>
  select(
    `...1`,
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    region == "PVN",
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons"
    )
  ) |>
  mutate(neg_log10_specificity_rank = -log10(specificity_rank))

two.group.unpaired <-
  df.two.group.unpaired |>
  dabest(
    x = pairs,
    y = neg_log10_specificity_rank,
    idx = c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons"
    ),
    paired = FALSE,
    id.column = "...1"
  )

two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:40 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_specificity_rank

Unpaired mean difference of Astro_INSR-==>POMC neurons (n = 146) minus Astro_INSR+==>POMC neurons (n = 175)
 -0.023 [95CI  -0.246; 0.198]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.meandiff,
  slopegraph = FALSE, color.column = control
)

Code

two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_d

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:40 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_specificity_rank

Unpaired Cohen's d of Astro_INSR-==>POMC neurons (n = 146) minus Astro_INSR+==>POMC neurons (n = 175)
 -0.0235 [95CI  -0.25; 0.203]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.cohens_d,
  slopegraph = FALSE, color.column = control
)

Multi-two group

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    region == "PVN",
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons"
    )
  ) |>
  mutate(
    neg_log10_specificity_rank = -log10(specificity_rank),
    control = if_else(
      control,
      "Norm",
      "CSDS"
    )
  ) |>
  unite(
    control,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_specificity_rank,
    idx = list(
      c(
        "Norm: Astro_INSR+==>POMC neurons",
        "Norm: Astro_INSR-==>POMC neurons",
        "Norm: Astro_INSR+==>AGRP/NPY neurons",
        "Norm: Astro_INSR-==>AGRP/NPY neurons"
      ),
      c(
        "CSDS: Astro_INSR+==>POMC neurons",
        "CSDS: Astro_INSR-==>POMC neurons",
        "CSDS: Astro_INSR+==>AGRP/NPY neurons",
        "CSDS: Astro_INSR-==>AGRP/NPY neurons"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d)

Magnitude

Code

df.two.group.unpaired <-
  df_complex_conditions |>
  select(
    `...1`,
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    region == "PVN",
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons"
    )
  ) |>
  mutate(neg_log10_magnitude_rank = -log10(magnitude_rank))

two.group.unpaired <-
  df.two.group.unpaired |>
  dabest(
    x = pairs,
    y = neg_log10_magnitude_rank,
    idx = c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons"
    ),
    paired = FALSE,
    id.column = "...1"
  )

two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:41 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_magnitude_rank

Unpaired mean difference of Astro_INSR-==>POMC neurons (n = 146) minus Astro_INSR+==>POMC neurons (n = 175)
 0.0631 [95CI  -0.222; 0.338]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.meandiff,
  slopegraph = FALSE, color.column = control
)

Code

two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_d

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:41 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_magnitude_rank

Unpaired Cohen's d of Astro_INSR-==>POMC neurons (n = 146) minus Astro_INSR+==>POMC neurons (n = 175)
 0.0491 [95CI  -0.175; 0.265]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.cohens_d,
  slopegraph = FALSE, color.column = control
)

Multi-two group

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    region == "PVN",
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons"
    )
  ) |>
  mutate(
    neg_log10_magnitude_rank = -log10(magnitude_rank),
    control = if_else(
      control,
      "Norm",
      "CSDS"
    )
  ) |>
  unite(
    control,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_magnitude_rank,
    idx = list(
      c(
        "Norm: Astro_INSR+==>POMC neurons",
        "Norm: Astro_INSR-==>POMC neurons",
        "Norm: Astro_INSR+==>AGRP/NPY neurons",
        "Norm: Astro_INSR-==>AGRP/NPY neurons"
      ),
      c(
        "CSDS: Astro_INSR+==>POMC neurons",
        "CSDS: Astro_INSR-==>POMC neurons",
        "CSDS: Astro_INSR+==>AGRP/NPY neurons",
        "CSDS: Astro_INSR-==>AGRP/NPY neurons"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d)

Neurons >>> Astrocyte

Arcuate

Specificity

Code

df.two.group.unpaired <-
  df_complex_conditions |>
  select(
    `...1`,
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    region == "ARC",
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-"
    )
  ) |>
  mutate(neg_log10_specificity_rank = -log10(specificity_rank))

two.group.unpaired <-
  df.two.group.unpaired |>
  dabest(
    x = pairs,
    y = neg_log10_specificity_rank,
    idx = c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-"
    ),
    paired = FALSE,
    id.column = "...1"
  )

two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:41 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_specificity_rank

Unpaired mean difference of POMC neurons==>Astro_INSR- (n = 424) minus POMC neurons==>Astro_INSR+ (n = 533)
 -0.257 [95CI  -0.401; -0.105]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.meandiff,
  slopegraph = FALSE, color.column = control
)

Code

two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_d

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:41 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_specificity_rank

Unpaired Cohen's d of POMC neurons==>Astro_INSR- (n = 424) minus POMC neurons==>Astro_INSR+ (n = 533)
 -0.224 [95CI  -0.35; -0.0902]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.cohens_d,
  slopegraph = FALSE, color.column = control
)

Multi-two group

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    region == "ARC",
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-",
      "AGRP/NPY neurons==>Astro_INSR+",
      "AGRP/NPY neurons==>Astro_INSR-"
    )
  ) |>
  mutate(
    neg_log10_specificity_rank = -log10(specificity_rank),
    control = if_else(
      control,
      "Norm",
      "HFD"
    )
  ) |>
  unite(
    control,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_specificity_rank,
    idx = list(
      c(
        "Norm: POMC neurons==>Astro_INSR+",
        "Norm: POMC neurons==>Astro_INSR-",
        "Norm: AGRP/NPY neurons==>Astro_INSR+",
        "Norm: AGRP/NPY neurons==>Astro_INSR-"
      ),
      c(
        "HFD: POMC neurons==>Astro_INSR+",
        "HFD: POMC neurons==>Astro_INSR-",
        "HFD: AGRP/NPY neurons==>Astro_INSR+",
        "HFD: AGRP/NPY neurons==>Astro_INSR-"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff, color.column = condition)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d, color.column = condition)

Magnitude

Code

df.two.group.unpaired <-
  df_complex_conditions |>
  select(
    `...1`,
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    region == "ARC",
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-"
    )
  ) |>
  mutate(neg_log10_magnitude_rank = -log10(magnitude_rank))

two.group.unpaired <-
  df.two.group.unpaired |>
  dabest(
    x = pairs,
    y = neg_log10_magnitude_rank,
    idx = c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-"
    ),
    paired = FALSE,
    id.column = "...1"
  )

two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:42 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_magnitude_rank

Unpaired mean difference of POMC neurons==>Astro_INSR- (n = 424) minus POMC neurons==>Astro_INSR+ (n = 533)
 -0.0905 [95CI  -0.24; 0.0532]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.meandiff,
  slopegraph = FALSE, color.column = control
)

Code

two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_d

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:42 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_magnitude_rank

Unpaired Cohen's d of POMC neurons==>Astro_INSR- (n = 424) minus POMC neurons==>Astro_INSR+ (n = 533)
 -0.0787 [95CI  -0.203; 0.048]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.cohens_d,
  slopegraph = FALSE, color.column = control
)

Multi-two group

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    region == "ARC",
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-",
      "AGRP/NPY neurons==>Astro_INSR+",
      "AGRP/NPY neurons==>Astro_INSR-"
    )
  ) |>
  mutate(
    neg_log10_magnitude_rank = -log10(magnitude_rank),
    control = if_else(
      control,
      "Norm",
      "HFD"
    )
  ) |>
  unite(
    control,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_magnitude_rank,
    idx = list(
      c(
        "Norm: POMC neurons==>Astro_INSR+",
        "Norm: POMC neurons==>Astro_INSR-",
        "Norm: AGRP/NPY neurons==>Astro_INSR+",
        "Norm: AGRP/NPY neurons==>Astro_INSR-"
      ),
      c(
        "HFD: POMC neurons==>Astro_INSR+",
        "HFD: POMC neurons==>Astro_INSR-",
        "HFD: AGRP/NPY neurons==>Astro_INSR+",
        "HFD: AGRP/NPY neurons==>Astro_INSR-"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
  color.column = condition
)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
  color.column = condition
)

PVN

Specificity

Code

df.two.group.unpaired <-
  df_complex_conditions |>
  select(
    `...1`,
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    region == "PVN",
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-"
    )
  ) |>
  mutate(neg_log10_specificity_rank = -log10(specificity_rank))

two.group.unpaired <-
  df.two.group.unpaired |>
  dabest(
    x = pairs,
    y = neg_log10_specificity_rank,
    idx = c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-"
    ),
    paired = FALSE,
    id.column = "...1"
  )

two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:42 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_specificity_rank

Unpaired mean difference of POMC neurons==>Astro_INSR- (n = 142) minus POMC neurons==>Astro_INSR+ (n = 190)
 -0.0763 [95CI  -0.32; 0.188]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.meandiff,
  slopegraph = FALSE, color.column = control
)

Code

two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_d

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:43 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_specificity_rank

Unpaired Cohen's d of POMC neurons==>Astro_INSR- (n = 142) minus POMC neurons==>Astro_INSR+ (n = 190)
 -0.0656 [95CI  -0.278; 0.162]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.cohens_d,
  slopegraph = FALSE, color.column = control
)

Multi-two group

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    region == "PVN",
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-",
      "AGRP/NPY neurons==>Astro_INSR+",
      "AGRP/NPY neurons==>Astro_INSR-"
    )
  ) |>
  mutate(
    neg_log10_specificity_rank = -log10(specificity_rank),
    control = if_else(
      control,
      "Norm",
      "CSDS"
    )
  ) |>
  unite(
    control,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_specificity_rank,
    idx = list(
      c(
        "Norm: POMC neurons==>Astro_INSR+",
        "Norm: POMC neurons==>Astro_INSR-",
        "Norm: AGRP/NPY neurons==>Astro_INSR+",
        "Norm: AGRP/NPY neurons==>Astro_INSR-"
      ),
      c(
        "CSDS: POMC neurons==>Astro_INSR+",
        "CSDS: POMC neurons==>Astro_INSR-",
        "CSDS: AGRP/NPY neurons==>Astro_INSR+",
        "CSDS: AGRP/NPY neurons==>Astro_INSR-"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d)

Magnitude

Code

df.two.group.unpaired <-
  df_complex_conditions |>
  select(
    `...1`,
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    region == "PVN",
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-"
    )
  ) |>
  mutate(neg_log10_magnitude_rank = -log10(magnitude_rank))

two.group.unpaired <-
  df.two.group.unpaired |>
  dabest(
    x = pairs,
    y = neg_log10_magnitude_rank,
    idx = c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-"
    ),
    paired = FALSE,
    id.column = "...1"
  )

two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiff

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:43 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_magnitude_rank

Unpaired mean difference of POMC neurons==>Astro_INSR- (n = 142) minus POMC neurons==>Astro_INSR+ (n = 190)
 -0.171 [95CI  -0.406; 0.0574]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.meandiff,
  slopegraph = FALSE, color.column = control
)

Code

two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_d

dabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================

Good afternoon!
The current time is 13:43 PM on Saturday July 29, 2023.

Dataset    :  df.two.group.unpaired
X Variable :  pairs
Y Variable :  neg_log10_magnitude_rank

Unpaired Cohen's d of POMC neurons==>Astro_INSR- (n = 142) minus POMC neurons==>Astro_INSR+ (n = 190)
 -0.154 [95CI  -0.356; 0.0602]


5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

Code

plot(two.group.unpaired.cohens_d,
  slopegraph = FALSE, color.column = control
)

Multi-two group

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    region == "PVN",
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-",
      "AGRP/NPY neurons==>Astro_INSR+",
      "AGRP/NPY neurons==>Astro_INSR-"
    )
  ) |>
  mutate(
    neg_log10_magnitude_rank = -log10(magnitude_rank),
    control = if_else(
      control,
      "Norm",
      "CSDS"
    )
  ) |>
  unite(
    control,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_magnitude_rank,
    idx = list(
      c(
        "Norm: POMC neurons==>Astro_INSR+",
        "Norm: POMC neurons==>Astro_INSR-",
        "Norm: AGRP/NPY neurons==>Astro_INSR+",
        "Norm: AGRP/NPY neurons==>Astro_INSR-"
      ),
      c(
        "CSDS: POMC neurons==>Astro_INSR+",
        "CSDS: POMC neurons==>Astro_INSR-",
        "CSDS: AGRP/NPY neurons==>Astro_INSR+",
        "CSDS: AGRP/NPY neurons==>Astro_INSR-"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d)

Arcuate + Paraventricular nuclei

Astrocytes >>> Neurons

Specificity

Treatment

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    !control,
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons"
    )
  ) |>
  mutate(neg_log10_specificity_rank = -log10(specificity_rank)) |>
  unite(
    region,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_specificity_rank,
    idx = list(
      c(
        "ARC: Astro_INSR+==>POMC neurons",
        "ARC: Astro_INSR-==>POMC neurons",
        "PVN: Astro_INSR+==>POMC neurons",
        "PVN: Astro_INSR-==>POMC neurons"
      ),
      c(
        "ARC: Astro_INSR+==>AGRP/NPY neurons",
        "ARC: Astro_INSR-==>AGRP/NPY neurons",
        "PVN: Astro_INSR+==>AGRP/NPY neurons",
        "PVN: Astro_INSR-==>AGRP/NPY neurons"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
  color.column = condition
)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
  color.column = condition
)

Control

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    control,
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons"
    )
  ) |>
  mutate(neg_log10_specificity_rank = -log10(specificity_rank)) |>
  unite(
    region,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_specificity_rank,
    idx = list(
      c(
        "ARC: Astro_INSR+==>POMC neurons",
        "ARC: Astro_INSR-==>POMC neurons",
        "PVN: Astro_INSR+==>POMC neurons",
        "PVN: Astro_INSR-==>POMC neurons"
      ),
      c(
        "ARC: Astro_INSR+==>AGRP/NPY neurons",
        "ARC: Astro_INSR-==>AGRP/NPY neurons",
        "PVN: Astro_INSR+==>AGRP/NPY neurons",
        "PVN: Astro_INSR-==>AGRP/NPY neurons"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
  color.column = condition
)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
  color.column = condition
)

Magnitude

Treatment

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    !control,
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons"
    )
  ) |>
  mutate(neg_log10_magnitude_rank = -log10(magnitude_rank)) |>
  unite(
    region,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_magnitude_rank,
    idx = list(
      c(
        "ARC: Astro_INSR+==>POMC neurons",
        "ARC: Astro_INSR-==>POMC neurons",
        "PVN: Astro_INSR+==>POMC neurons",
        "PVN: Astro_INSR-==>POMC neurons"
      ),
      c(
        "ARC: Astro_INSR+==>AGRP/NPY neurons",
        "ARC: Astro_INSR-==>AGRP/NPY neurons",
        "PVN: Astro_INSR+==>AGRP/NPY neurons",
        "PVN: Astro_INSR-==>AGRP/NPY neurons"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
  color.column = condition
)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
  color.column = condition
)

Control

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    control,
    pairs %in% c(
      "Astro_INSR+==>POMC neurons",
      "Astro_INSR-==>POMC neurons",
      "Astro_INSR+==>AGRP/NPY neurons",
      "Astro_INSR-==>AGRP/NPY neurons"
    )
  ) |>
  mutate(neg_log10_magnitude_rank = -log10(magnitude_rank)) |>
  unite(
    region,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_magnitude_rank,
    idx = list(
      c(
        "ARC: Astro_INSR+==>POMC neurons",
        "ARC: Astro_INSR-==>POMC neurons",
        "PVN: Astro_INSR+==>POMC neurons",
        "PVN: Astro_INSR-==>POMC neurons"
      ),
      c(
        "ARC: Astro_INSR+==>AGRP/NPY neurons",
        "ARC: Astro_INSR-==>AGRP/NPY neurons",
        "PVN: Astro_INSR+==>AGRP/NPY neurons",
        "PVN: Astro_INSR-==>AGRP/NPY neurons"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
  color.column = condition
)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
  color.column = condition
)

Neurons >>> Astrocyte

Specificity

Treatment

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    !control,
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-",
      "AGRP/NPY neurons==>Astro_INSR+",
      "AGRP/NPY neurons==>Astro_INSR-"
    )
  ) |>
  mutate(neg_log10_specificity_rank = -log10(specificity_rank)) |>
  unite(
    region,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_specificity_rank,
    idx = list(
      c(
        "ARC: POMC neurons==>Astro_INSR+",
        "ARC: POMC neurons==>Astro_INSR-",
        "PVN: POMC neurons==>Astro_INSR+",
        "PVN: POMC neurons==>Astro_INSR-"
      ),
      c(
        "ARC: AGRP/NPY neurons==>Astro_INSR+",
        "ARC: AGRP/NPY neurons==>Astro_INSR-",
        "PVN: AGRP/NPY neurons==>Astro_INSR+",
        "PVN: AGRP/NPY neurons==>Astro_INSR-"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
  color.column = condition
)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
  color.column = condition
)

Control

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    specificity_rank
  ) |>
  filter(
    control,
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-",
      "AGRP/NPY neurons==>Astro_INSR+",
      "AGRP/NPY neurons==>Astro_INSR-"
    )
  ) |>
  mutate(neg_log10_specificity_rank = -log10(specificity_rank)) |>
  unite(
    region,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_specificity_rank,
    idx = list(
      c(
        "ARC: POMC neurons==>Astro_INSR+",
        "ARC: POMC neurons==>Astro_INSR-",
        "PVN: POMC neurons==>Astro_INSR+",
        "PVN: POMC neurons==>Astro_INSR-"
      ),
      c(
        "ARC: AGRP/NPY neurons==>Astro_INSR+",
        "ARC: AGRP/NPY neurons==>Astro_INSR-",
        "PVN: AGRP/NPY neurons==>Astro_INSR+",
        "PVN: AGRP/NPY neurons==>Astro_INSR-"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
  color.column = condition
)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
  color.column = condition
)

Magnitude

Treatment

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    !control,
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-",
      "AGRP/NPY neurons==>Astro_INSR+",
      "AGRP/NPY neurons==>Astro_INSR-"
    )
  ) |>
  mutate(neg_log10_magnitude_rank = -log10(magnitude_rank)) |>
  unite(
    region,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_magnitude_rank,
    idx = list(
      c(
        "ARC: POMC neurons==>Astro_INSR+",
        "ARC: POMC neurons==>Astro_INSR-",
        "PVN: POMC neurons==>Astro_INSR+",
        "PVN: POMC neurons==>Astro_INSR-"
      ),
      c(
        "ARC: AGRP/NPY neurons==>Astro_INSR+",
        "ARC: AGRP/NPY neurons==>Astro_INSR-",
        "PVN: AGRP/NPY neurons==>Astro_INSR+",
        "PVN: AGRP/NPY neurons==>Astro_INSR-"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
  color.column = condition
)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
  color.column = condition
)

Control

Code

df.multi.group <-
  df_complex_conditions |>
  select(
    pairs,
    ligand_complex,
    receptor_complex,
    region,
    condition,
    control,
    magnitude_rank
  ) |>
  filter(
    control,
    pairs %in% c(
      "POMC neurons==>Astro_INSR+",
      "POMC neurons==>Astro_INSR-",
      "AGRP/NPY neurons==>Astro_INSR+",
      "AGRP/NPY neurons==>Astro_INSR-"
    )
  ) |>
  mutate(neg_log10_magnitude_rank = -log10(magnitude_rank)) |>
  unite(
    region,
    pairs,
    col = "case",
    sep = ": "
  ) |>
  unite(
    ligand_complex,
    receptor_complex,
    col = "signal"
  )

multi.group <-
  df.multi.group %>%
  dabest(
    x = case,
    y = neg_log10_magnitude_rank,
    idx = list(
      c(
        "ARC: POMC neurons==>Astro_INSR+",
        "ARC: POMC neurons==>Astro_INSR-",
        "PVN: POMC neurons==>Astro_INSR+",
        "PVN: POMC neurons==>Astro_INSR-"
      ),
      c(
        "ARC: AGRP/NPY neurons==>Astro_INSR+",
        "ARC: AGRP/NPY neurons==>Astro_INSR-",
        "PVN: AGRP/NPY neurons==>Astro_INSR+",
        "PVN: AGRP/NPY neurons==>Astro_INSR-"
      )
    ),
    paired = FALSE,
    id.column = "signal"
  )

multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
  color.column = condition
)

Code

multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
  color.column = condition
)

Session info

Code

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.3 (2023-03-15)
 os       macOS 14.0
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Europe/Vienna
 date     2023-07-29
 pandoc   2.19.2 @ /opt/homebrew/Caskroom/mambaforge/base/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package          * version    date (UTC) lib source
 base64enc          0.1-3      2015-07-28 [1] CRAN (R 4.2.0)
 bayestestR         0.13.1     2023-04-07 [1] RSPM (R 4.2.3)
 beeswarm           0.4.0      2021-06-01 [1] CRAN (R 4.2.0)
 bit                4.0.5      2022-11-15 [1] CRAN (R 4.2.0)
 bit64              4.0.5      2020-08-30 [1] CRAN (R 4.2.0)
 boot               1.3-28.1   2022-11-22 [1] CRAN (R 4.2.3)
 cli                3.6.1      2023-03-23 [1] RSPM (R 4.2.3)
 coda               0.19-4     2020-09-30 [1] CRAN (R 4.2.0)
 codetools          0.2-19     2023-02-01 [1] CRAN (R 4.2.3)
 colorspace         2.1-0      2023-01-23 [1] CRAN (R 4.2.0)
 ComplexUpset     * 1.3.5      2022-11-28 [1] Github (krassowski/complex-upset@bb3f10a)
 correlation        0.8.4      2023-04-06 [1] RSPM (R 4.2.3)
 cowplot          * 1.1.1      2020-12-30 [1] CRAN (R 4.2.0)
 crayon             1.5.2      2022-09-29 [1] CRAN (R 4.2.0)
 dabestr          * 0.3.0      2022-05-21 [1] Github (ACCLAB/dabestr@8775899)
 datawizard         0.7.1      2023-04-03 [1] RSPM (R 4.2.3)
 digest             0.6.31     2022-12-11 [1] CRAN (R 4.2.0)
 dplyr            * 1.1.2      2023-04-20 [1] RSPM (R 4.2.3)
 effsize            0.8.1      2020-10-05 [1] CRAN (R 4.2.0)
 ellipsis           0.3.2      2021-04-29 [1] CRAN (R 4.2.0)
 emmeans            1.8.5      2023-03-08 [1] RSPM (R 4.2.3)
 estimability       1.4.1      2022-08-05 [1] CRAN (R 4.2.0)
 evaluate           0.20       2023-01-17 [1] CRAN (R 4.2.0)
 fansi              1.0.4      2023-01-22 [1] CRAN (R 4.2.0)
 farver             2.1.1      2022-07-06 [1] CRAN (R 4.2.0)
 fastmap            1.1.1      2023-02-24 [1] RSPM (R 4.2.3)
 forcats          * 1.0.0      2023-01-29 [1] CRAN (R 4.2.0)
 future           * 1.32.0     2023-03-07 [1] RSPM (R 4.2.3)
 generics           0.1.3      2022-07-05 [1] CRAN (R 4.2.0)
 ggbeeswarm         0.7.1.9000 2023-02-08 [1] Github (eclarke/ggbeeswarm@5dc91cc)
 ggmin              0.0.0.9000 2022-08-12 [1] Github (sjessa/ggmin@8ada274)
 ggplot2          * 3.4.2      2023-04-03 [1] RSPM (R 4.2.3)
 ggrepel          * 0.9.3      2023-02-03 [1] RSPM (R 4.2.3)
 ggstatsplot      * 0.11.1     2023-04-14 [1] RSPM (R 4.2.3)
 ggupset          * 0.3.0.9002 2022-05-21 [1] Github (const-ae/ggupset@2a03c58)
 globals            0.16.2     2022-11-21 [1] CRAN (R 4.2.0)
 glue               1.6.2      2022-02-24 [1] CRAN (R 4.2.0)
 gtable             0.3.3      2023-03-21 [1] RSPM (R 4.2.3)
 here             * 1.0.1      2020-12-13 [1] CRAN (R 4.2.0)
 hms                1.1.3      2023-03-21 [1] RSPM (R 4.2.3)
 htmltools          0.5.5      2023-03-23 [1] RSPM (R 4.2.3)
 htmlwidgets        1.6.2      2023-03-17 [1] RSPM (R 4.2.3)
 insight            0.19.1     2023-03-18 [1] RSPM (R 4.2.3)
 jsonlite           1.8.4      2022-12-06 [1] CRAN (R 4.2.0)
 knitr              1.42       2023-01-25 [1] RSPM
 labeling           0.4.2      2020-10-20 [1] CRAN (R 4.2.0)
 lattice            0.21-8     2023-04-05 [1] RSPM (R 4.2.3)
 lifecycle          1.0.3      2022-10-07 [1] CRAN (R 4.2.0)
 listenv            0.9.0      2022-12-16 [1] CRAN (R 4.2.0)
 lubridate        * 1.9.2      2023-02-10 [1] RSPM (R 4.2.3)
 magrittr         * 2.0.3      2022-03-30 [1] CRAN (R 4.2.0)
 MASS               7.3-58.3   2023-03-07 [1] RSPM (R 4.2.3)
 Matrix             1.5-4      2023-04-04 [1] RSPM (R 4.2.3)
 multcomp           1.4-23     2023-03-09 [1] RSPM (R 4.2.3)
 munsell            0.5.0      2018-06-12 [1] CRAN (R 4.2.0)
 mvtnorm            1.1-3      2021-10-08 [1] CRAN (R 4.2.0)
 paletteer          1.5.0      2022-10-19 [1] CRAN (R 4.2.0)
 parallelly         1.35.0     2023-03-23 [1] RSPM (R 4.2.3)
 parameters         0.21.0     2023-04-19 [1] RSPM (R 4.2.3)
 patchwork        * 1.1.2.9000 2023-02-08 [1] Github (thomasp85/patchwork@c14c960)
 pillar             1.9.0      2023-03-22 [1] RSPM (R 4.2.3)
 pkgconfig          2.0.3      2019-09-22 [1] CRAN (R 4.2.0)
 plyr               1.8.8      2022-11-11 [1] CRAN (R 4.2.0)
 png                0.1-8      2022-11-29 [1] CRAN (R 4.2.0)
 purrr            * 1.0.1      2023-01-10 [1] CRAN (R 4.2.0)
 R6                 2.5.1      2021-08-19 [1] CRAN (R 4.2.0)
 RColorBrewer     * 1.1-3      2022-04-03 [1] CRAN (R 4.2.0)
 Rcpp               1.0.10     2023-01-22 [1] CRAN (R 4.2.0)
 readr            * 2.1.4      2023-02-10 [1] RSPM (R 4.2.3)
 rematch2           2.1.2      2020-05-01 [1] CRAN (R 4.2.0)
 repr               1.1.6      2023-01-26 [1] CRAN (R 4.2.0)
 reticulate       * 1.28-9000  2023-04-22 [1] Github (rstudio/reticulate@63a5c3e)
 rlang              1.1.0      2023-03-14 [1] RSPM (R 4.2.3)
 rmarkdown          2.21       2023-03-26 [1] RSPM (R 4.2.3)
 rprojroot          2.0.3      2022-04-02 [1] CRAN (R 4.2.0)
 sandwich           3.0-2      2022-06-15 [1] CRAN (R 4.2.0)
 scales             1.2.1      2022-08-20 [1] CRAN (R 4.2.0)
 sessioninfo        1.2.2      2021-12-06 [1] CRAN (R 4.2.0)
 skimr            * 2.1.5      2023-02-08 [1] Github (ropensci/skimr@d5126aa)
 statsExpressions   1.5.0      2023-02-19 [1] RSPM (R 4.2.3)
 stringi            1.7.12     2023-01-11 [1] CRAN (R 4.2.0)
 stringr          * 1.5.0      2022-12-02 [1] CRAN (R 4.2.0)
 survival           3.5-5      2023-03-12 [1] RSPM (R 4.2.3)
 TH.data            1.1-2      2023-04-17 [1] RSPM (R 4.2.3)
 tibble           * 3.2.1      2023-03-20 [1] RSPM (R 4.2.3)
 tidyr            * 1.3.0      2023-01-24 [1] CRAN (R 4.2.0)
 tidyselect         1.2.0      2022-10-10 [1] CRAN (R 4.2.0)
 tidyverse        * 2.0.0      2023-02-22 [1] RSPM (R 4.2.3)
 timechange         0.2.0      2023-01-11 [1] CRAN (R 4.2.0)
 tzdb               0.3.0      2022-03-28 [1] CRAN (R 4.2.0)
 utf8               1.2.3      2023-01-31 [1] CRAN (R 4.2.0)
 vctrs              0.6.2      2023-04-19 [1] RSPM (R 4.2.3)
 vipor              0.4.5      2017-03-22 [1] CRAN (R 4.2.0)
 vroom              1.6.1      2023-01-22 [1] CRAN (R 4.2.0)
 withr              2.5.0      2022-03-03 [1] CRAN (R 4.2.0)
 xfun               0.39       2023-04-20 [1] RSPM (R 4.2.3)
 xtable             1.8-4      2019-04-21 [1] CRAN (R 4.2.0)
 yaml               2.3.7      2023-01-23 [1] RSPM
 zeallot            0.1.0      2018-01-28 [1] CRAN (R 4.2.0)
 zoo                1.8-12     2023-04-13 [1] RSPM (R 4.2.3)

 [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library

──────────────────────────────────────────────────────────────────────────────