# 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(cowplot)
library(patchwork)
library(ggstatsplot)
library(ggupset)
library(ComplexUpset)
library(dabestr)
})src_dir <- here("code")
data_dir <- here("data")
output_dir <- here("output")
plots_dir <- here(output_dir, "figures")
tables_dir <- here(output_dir, "tables")source(here(src_dir, "genes.R"))
source(here(src_dir, "functions.R"))# 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)# ggplot2 theme
theme_set(ggmin::theme_powerpoint())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"
)
)skim(lutomska_norm)| Name | lutomska_norm |
| Number of rows | 40576 |
| 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 | 4 | 0 |
| target | 0 | 1 | 10 | 16 | 0 | 4 | 0 |
| ligand_complex | 0 | 1 | 1 | 16 | 0 | 572 | 0 |
| receptor_complex | 0 | 1 | 2 | 19 | 0 | 626 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1 | 20287.50 | 11713.43 | 0.00 | 10143.75 | 20287.50 | 30431.25 | 40575.00 | ▇▇▇▇▇ |
| lr_means | 0 | 1 | 0.20 | 0.34 | 0.00 | 0.00 | 0.05 | 0.26 | 3.54 | ▇▁▁▁▁ |
| cellphone_pvals | 0 | 1 | 0.64 | 0.43 | 0.00 | 0.12 | 0.99 | 1.00 | 1.00 | ▅▁▁▁▇ |
| expr_prod | 0 | 1 | 0.08 | 0.43 | 0.00 | 0.00 | 0.00 | 0.02 | 12.23 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 0.11 | 0.42 | -1.91 | -0.07 | 0.01 | 0.22 | 4.62 | ▁▇▁▁▁ |
| lr_logfc | 0 | 1 | -0.06 | 0.41 | -3.93 | -0.14 | -0.01 | 0.09 | 3.76 | ▁▁▇▁▁ |
| spec_weight | 0 | 1 | 0.06 | 0.11 | 0.00 | 0.00 | 0.01 | 0.08 | 1.00 | ▇▁▁▁▁ |
| lrscore | 0 | 1 | 0.25 | 0.28 | 0.00 | 0.00 | 0.16 | 0.46 | 0.96 | ▇▂▂▂▁ |
| lr_probs | 0 | 1 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.31 | ▇▁▁▁▁ |
| 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 | 0.38 | 0.00 | 0.49 | 1.00 | 1.00 | 1.00 | ▂▁▁▁▇ |
| magnitude_rank | 0 | 1 | 0.88 | 0.29 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
skim(lutomska_hfhsd)| Name | lutomska_hfhsd |
| Number of rows | 45440 |
| 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 | 4 | 0 |
| target | 0 | 1 | 10 | 16 | 0 | 4 | 0 |
| ligand_complex | 0 | 1 | 1 | 16 | 0 | 631 | 0 |
| receptor_complex | 0 | 1 | 2 | 19 | 0 | 687 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1 | 22719.50 | 13117.54 | 0.00 | 11359.75 | 22719.50 | 34079.25 | 45439.00 | ▇▇▇▇▇ |
| lr_means | 0 | 1 | 0.19 | 0.33 | 0.00 | 0.00 | 0.05 | 0.26 | 3.46 | ▇▁▁▁▁ |
| cellphone_pvals | 0 | 1 | 0.62 | 0.45 | 0.00 | 0.04 | 0.99 | 1.00 | 1.00 | ▅▁▁▁▇ |
| expr_prod | 0 | 1 | 0.07 | 0.39 | 0.00 | 0.00 | 0.00 | 0.01 | 11.64 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 0.10 | 0.43 | -2.08 | -0.06 | 0.01 | 0.20 | 4.90 | ▁▇▁▁▁ |
| lr_logfc | 0 | 1 | -0.06 | 0.42 | -4.17 | -0.14 | -0.01 | 0.07 | 4.16 | ▁▁▇▁▁ |
| spec_weight | 0 | 1 | 0.06 | 0.11 | 0.00 | 0.00 | 0.01 | 0.08 | 1.00 | ▇▁▁▁▁ |
| lrscore | 0 | 1 | 0.24 | 0.27 | 0.00 | 0.00 | 0.13 | 0.42 | 0.96 | ▇▂▂▂▁ |
| 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 | 0.76 | 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 | ▁▁▁▁▇ |
skim(deng_norm)| 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 | 10 | 16 | 0 | 4 | 0 |
| target | 0 | 1 | 10 | 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 | 0.08 | 0.29 | 3.74 | ▇▁▁▁▁ |
| cellphone_pvals | 0 | 1 | 0.60 | 0.43 | 0.00 | 0.08 | 0.88 | 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 | -4.11 | -0.10 | 0.00 | 0.07 | 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 | 0.21 | 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 | 0.75 | 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 | ▁▁▁▁▇ |
skim(deng_hfd)| 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 | 10 | 16 | 0 | 4 | 0 |
| target | 0 | 1 | 10 | 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 | 0.36 | 0.00 | 0.00 | 0.07 | 0.27 | 3.75 | ▇▁▁▁▁ |
| cellphone_pvals | 0 | 1 | 0.61 | 0.42 | 0.00 | 0.12 | 0.88 | 1.00 | 1.00 | ▅▁▁▁▇ |
| expr_prod | 0 | 1 | 0.09 | 0.48 | 0.00 | 0.00 | 0.00 | 0.02 | 13.78 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 0.08 | 0.32 | -1.31 | -0.06 | 0.02 | 0.17 | 3.53 | ▁▇▁▁▁ |
| lr_logfc | 0 | 1 | -0.03 | 0.33 | -2.90 | -0.09 | -0.01 | 0.06 | 3.63 | ▁▁▇▁▁ |
| spec_weight | 0 | 1 | 0.06 | 0.10 | 0.00 | 0.00 | 0.02 | 0.09 | 1.00 | ▇▁▁▁▁ |
| lrscore | 0 | 1 | 0.26 | 0.27 | 0.00 | 0.00 | 0.18 | 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 | 0.90 | 0.29 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
| steady_rank | 0 | 1 | 0.76 | 0.38 | 0.00 | 0.49 | 1.00 | 1.00 | 1.00 | ▂▁▁▁▇ |
| specificity_rank | 0 | 1 | 0.75 | 0.39 | 0.00 | 0.42 | 1.00 | 1.00 | 1.00 | ▂▁▁▁▇ |
| magnitude_rank | 0 | 1 | 0.87 | 0.31 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
skim(lopez_norm)| Name | lopez_norm |
| Number of rows | 45648 |
| 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 | 4 | 0 |
| target | 0 | 1 | 10 | 16 | 0 | 4 | 0 |
| ligand_complex | 0 | 1 | 1 | 16 | 0 | 630 | 0 |
| receptor_complex | 0 | 1 | 2 | 19 | 0 | 692 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1 | 22823.50 | 13177.59 | 0.00 | 11411.75 | 22823.50 | 34235.25 | 45647.00 | ▇▇▇▇▇ |
| lr_means | 0 | 1 | 0.20 | 0.35 | 0.00 | 0.00 | 0.07 | 0.27 | 3.74 | ▇▁▁▁▁ |
| cellphone_pvals | 0 | 1 | 0.53 | 0.45 | 0.00 | 0.02 | 0.55 | 1.00 | 1.00 | ▇▂▁▁▇ |
| expr_prod | 0 | 1 | 0.07 | 0.41 | 0.00 | 0.00 | 0.00 | 0.02 | 12.57 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 0.21 | 0.46 | -1.68 | -0.03 | 0.06 | 0.29 | 4.71 | ▁▇▁▁▁ |
| lr_logfc | 0 | 1 | -0.03 | 0.44 | -4.10 | -0.08 | 0.00 | 0.06 | 6.07 | ▁▅▇▁▁ |
| spec_weight | 0 | 1 | 0.06 | 0.10 | 0.00 | 0.00 | 0.02 | 0.09 | 1.00 | ▇▁▁▁▁ |
| lrscore | 0 | 1 | 0.29 | 0.28 | 0.00 | 0.00 | 0.22 | 0.50 | 0.97 | ▇▃▂▂▁ |
| lr_probs | 0 | 1 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.31 | ▇▁▁▁▁ |
| cellchat_pvals | 0 | 1 | 0.91 | 0.28 | 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 | 0.47 | 1.00 | 1.00 | 1.00 | ▂▁▁▁▇ |
| magnitude_rank | 0 | 1 | 0.87 | 0.30 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
skim(lopez_csds)| Name | lopez_csds |
| Number of rows | 46784 |
| 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 | 4 | 0 |
| target | 0 | 1 | 10 | 16 | 0 | 4 | 0 |
| ligand_complex | 0 | 1 | 1 | 16 | 0 | 635 | 0 |
| receptor_complex | 0 | 1 | 2 | 19 | 0 | 711 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1 | 23391.50 | 13505.52 | 0.00 | 11695.75 | 23391.50 | 35087.25 | 46783.00 | ▇▇▇▇▇ |
| lr_means | 0 | 1 | 0.20 | 0.35 | 0.00 | 0.00 | 0.07 | 0.25 | 3.54 | ▇▁▁▁▁ |
| cellphone_pvals | 0 | 1 | 0.57 | 0.43 | 0.00 | 0.05 | 0.72 | 1.00 | 1.00 | ▆▂▁▁▇ |
| expr_prod | 0 | 1 | 0.08 | 0.43 | 0.00 | 0.00 | 0.00 | 0.01 | 12.42 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 0.18 | 0.47 | -1.50 | -0.04 | 0.03 | 0.23 | 5.81 | ▃▇▁▁▁ |
| lr_logfc | 0 | 1 | -0.03 | 0.39 | -3.15 | -0.10 | 0.00 | 0.05 | 4.04 | ▁▁▇▁▁ |
| spec_weight | 0 | 1 | 0.06 | 0.10 | 0.00 | 0.00 | 0.02 | 0.08 | 1.00 | ▇▁▁▁▁ |
| lrscore | 0 | 1 | 0.27 | 0.28 | 0.00 | 0.00 | 0.18 | 0.47 | 0.96 | ▇▃▂▂▁ |
| lr_probs | 0 | 1 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.29 | ▇▁▁▁▁ |
| 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 | 0.58 | 1.00 | 1.00 | 1.00 | ▂▁▁▁▇ |
| specificity_rank | 0 | 1 | 0.77 | 0.38 | 0.00 | 0.50 | 1.00 | 1.00 | 1.00 | ▂▁▁▁▇ |
| magnitude_rank | 0 | 1 | 0.88 | 0.30 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
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)skim(lutomska_norm)| Name | lutomska_norm |
| Number of rows | 1544 |
| 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 | 4 | 0 |
| target | 0 | 1 | 10 | 16 | 0 | 4 | 0 |
| ligand_complex | 0 | 1 | 2 | 8 | 0 | 160 | 0 |
| receptor_complex | 0 | 1 | 2 | 13 | 0 | 186 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1 | 18111.50 | 11464.85 | 20.00 | 7744.50 | 17828.50 | 27509.75 | 40551.00 | ▇▆▆▆▅ |
| lr_means | 0 | 1 | 1.03 | 0.54 | 0.46 | 0.63 | 0.85 | 1.27 | 3.54 | ▇▃▁▁▁ |
| cellphone_pvals | 0 | 1 | 0.01 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| expr_prod | 0 | 1 | 0.96 | 1.21 | 0.10 | 0.31 | 0.56 | 1.10 | 12.23 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 0.78 | 0.71 | 0.02 | 0.30 | 0.53 | 1.05 | 4.62 | ▇▂▁▁▁ |
| lr_logfc | 0 | 1 | 0.52 | 0.48 | -0.30 | 0.22 | 0.40 | 0.66 | 3.76 | ▇▃▁▁▁ |
| spec_weight | 0 | 1 | 0.16 | 0.09 | 0.02 | 0.10 | 0.14 | 0.19 | 0.94 | ▇▂▁▁▁ |
| lrscore | 0 | 1 | 0.83 | 0.06 | 0.67 | 0.79 | 0.83 | 0.88 | 0.96 | ▁▆▇▇▃ |
| lr_probs | 0 | 1 | 0.03 | 0.04 | 0.00 | 0.01 | 0.01 | 0.03 | 0.31 | ▇▁▁▁▁ |
| cellchat_pvals | 0 | 1 | 0.03 | 0.16 | 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 | ▇▂▂▁▁ |
skim(lutomska_hfhsd)| Name | lutomska_hfhsd |
| Number of rows | 1660 |
| 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 | 4 | 0 |
| target | 0 | 1 | 10 | 16 | 0 | 4 | 0 |
| ligand_complex | 0 | 1 | 2 | 8 | 0 | 172 | 0 |
| receptor_complex | 0 | 1 | 2 | 13 | 0 | 204 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1 | 21311.44 | 12983.69 | 79.00 | 8900.75 | 21891.00 | 32261.00 | 45160.00 | ▇▆▆▇▅ |
| lr_means | 0 | 1 | 0.99 | 0.53 | 0.44 | 0.59 | 0.82 | 1.22 | 3.46 | ▇▃▁▁▁ |
| cellphone_pvals | 0 | 1 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.14 | ▇▁▁▁▁ |
| expr_prod | 0 | 1 | 0.89 | 1.12 | 0.05 | 0.27 | 0.51 | 1.05 | 11.64 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 0.73 | 0.75 | 0.02 | 0.24 | 0.44 | 0.93 | 4.90 | ▇▂▁▁▁ |
| lr_logfc | 0 | 1 | 0.47 | 0.50 | -0.35 | 0.17 | 0.33 | 0.58 | 4.16 | ▇▂▁▁▁ |
| spec_weight | 0 | 1 | 0.16 | 0.09 | 0.03 | 0.10 | 0.14 | 0.20 | 0.90 | ▇▂▁▁▁ |
| lrscore | 0 | 1 | 0.83 | 0.06 | 0.61 | 0.79 | 0.84 | 0.88 | 0.96 | ▁▁▇▇▃ |
| lr_probs | 0 | 1 | 0.03 | 0.04 | 0.00 | 0.01 | 0.01 | 0.03 | 0.31 | ▇▁▁▁▁ |
| 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 | ▇▂▁▁▁ |
skim(deng_norm)| Name | deng_norm |
| Number of rows | 1798 |
| 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 | 4 | 0 |
| target | 0 | 1 | 10 | 16 | 0 | 4 | 0 |
| ligand_complex | 0 | 1 | 2 | 16 | 0 | 183 | 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 | 19714.98 | 12054.15 | 112.00 | 10006.25 | 18545.50 | 29239.25 | 44009.00 | ▇▇▇▅▅ |
| lr_means | 0 | 1 | 1.11 | 0.61 | 0.48 | 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 | 1.05 | 1.50 | 0.01 | 0.29 | 0.53 | 1.23 | 13.45 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 0.55 | 0.40 | 0.08 | 0.23 | 0.44 | 0.79 | 2.55 | ▇▃▁▁▁ |
| lr_logfc | 0 | 1 | 0.56 | 0.72 | -1.08 | 0.20 | 0.37 | 0.60 | 5.31 | ▃▇▁▁▁ |
| spec_weight | 0 | 1 | 0.14 | 0.08 | 0.01 | 0.09 | 0.12 | 0.17 | 0.80 | ▇▂▁▁▁ |
| lrscore | 0 | 1 | 0.81 | 0.07 | 0.34 | 0.76 | 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 | 0.07 | 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 | ▇▂▂▁▁ |
skim(deng_hfd)| Name | deng_hfd |
| Number of rows | 1767 |
| 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 | 4 | 0 |
| target | 0 | 1 | 10 | 16 | 0 | 4 | 0 |
| ligand_complex | 0 | 1 | 2 | 8 | 0 | 170 | 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 | 20169.85 | 12638.25 | 20.00 | 9210.50 | 19011.00 | 30182.00 | 45302.00 | ▇▇▆▆▅ |
| lr_means | 0 | 1 | 1.13 | 0.55 | 0.49 | 0.70 | 0.96 | 1.43 | 3.75 | ▇▃▂▁▁ |
| cellphone_pvals | 0 | 1 | 0.02 | 0.05 | 0.00 | 0.00 | 0.00 | 0.01 | 1.00 | ▇▁▁▁▁ |
| expr_prod | 0 | 1 | 1.14 | 1.48 | 0.11 | 0.33 | 0.59 | 1.37 | 13.78 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 0.60 | 0.46 | 0.05 | 0.24 | 0.50 | 0.82 | 3.53 | ▇▃▁▁▁ |
| lr_logfc | 0 | 1 | 0.48 | 0.42 | -0.49 | 0.20 | 0.38 | 0.65 | 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 | 0.06 | 0.65 | 0.77 | 0.82 | 0.87 | 0.96 | ▂▆▇▇▃ |
| lr_probs | 0 | 1 | 0.04 | 0.05 | 0.00 | 0.01 | 0.02 | 0.06 | 0.39 | ▇▁▁▁▁ |
| cellchat_pvals | 0 | 1 | 0.04 | 0.14 | 0.00 | 0.00 | 0.00 | 0.01 | 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 | ▇▂▂▁▁ |
skim(lopez_norm)| Name | lopez_norm |
| Number of rows | 1615 |
| 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 | 4 | 0 |
| target | 0 | 1 | 10 | 16 | 0 | 4 | 0 |
| ligand_complex | 0 | 1 | 2 | 16 | 0 | 179 | 0 |
| receptor_complex | 0 | 1 | 2 | 13 | 0 | 219 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1 | 22037.27 | 15111.21 | 20.00 | 8861.50 | 21095.00 | 36386.50 | 45642.00 | ▇▆▅▅▇ |
| lr_means | 0 | 1 | 1.04 | 0.58 | 0.42 | 0.58 | 0.86 | 1.30 | 3.74 | ▇▃▁▁▁ |
| cellphone_pvals | 0 | 1 | 0.00 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.77 | ▇▁▁▁▁ |
| expr_prod | 0 | 1 | 0.89 | 1.23 | 0.07 | 0.25 | 0.44 | 1.03 | 12.57 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 1.08 | 0.78 | 0.03 | 0.46 | 0.88 | 1.54 | 4.71 | ▇▅▂▁▁ |
| lr_logfc | 0 | 1 | 0.66 | 0.76 | -0.56 | 0.21 | 0.44 | 0.87 | 6.07 | ▇▃▁▁▁ |
| spec_weight | 0 | 1 | 0.15 | 0.08 | 0.01 | 0.10 | 0.14 | 0.19 | 0.83 | ▇▃▁▁▁ |
| lrscore | 0 | 1 | 0.84 | 0.06 | 0.68 | 0.80 | 0.84 | 0.89 | 0.97 | ▁▅▇▇▃ |
| lr_probs | 0 | 1 | 0.02 | 0.03 | 0.00 | 0.00 | 0.01 | 0.03 | 0.31 | ▇▁▁▁▁ |
| cellchat_pvals | 0 | 1 | 0.04 | 0.20 | 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 | ▇▂▂▁▁ |
skim(lopez_csds)| Name | lopez_csds |
| Number of rows | 1619 |
| 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 | 4 | 0 |
| target | 0 | 1 | 10 | 16 | 0 | 4 | 0 |
| ligand_complex | 0 | 1 | 2 | 10 | 0 | 182 | 0 |
| receptor_complex | 0 | 1 | 2 | 13 | 0 | 210 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1 | 22938.30 | 15290.27 | 81.00 | 9468.50 | 22238.00 | 37294.50 | 46778.00 | ▇▆▅▆▇ |
| lr_means | 0 | 1 | 1.10 | 0.57 | 0.44 | 0.64 | 0.95 | 1.40 | 3.54 | ▇▅▂▁▁ |
| cellphone_pvals | 0 | 1 | 0.01 | 0.05 | 0.00 | 0.00 | 0.00 | 0.00 | 0.99 | ▇▁▁▁▁ |
| expr_prod | 0 | 1 | 1.05 | 1.39 | 0.07 | 0.28 | 0.55 | 1.27 | 12.42 | ▇▁▁▁▁ |
| scaled_weight | 0 | 1 | 1.07 | 0.81 | 0.02 | 0.43 | 0.90 | 1.54 | 5.81 | ▇▃▁▁▁ |
| lr_logfc | 0 | 1 | 0.61 | 0.54 | -0.27 | 0.21 | 0.45 | 0.89 | 4.04 | ▇▅▁▁▁ |
| spec_weight | 0 | 1 | 0.15 | 0.07 | 0.03 | 0.10 | 0.14 | 0.18 | 0.63 | ▇▅▁▁▁ |
| lrscore | 0 | 1 | 0.84 | 0.06 | 0.66 | 0.80 | 0.85 | 0.89 | 0.96 | ▁▃▇▇▅ |
| lr_probs | 0 | 1 | 0.03 | 0.04 | 0.00 | 0.01 | 0.01 | 0.03 | 0.29 | ▇▁▁▁▁ |
| 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 | ▇▂▁▁▁ |
Merge datasets
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_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>AGRP/NPY neurons",
"POMC neurons==>Astro_LXN+",
"AGRP/NPY neurons==>Astro_LXN+",
"POMC neurons==>Astro_LXN-",
"AGRP/NPY neurons==>Astro_LXN-"
))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_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>AGRP/NPY neurons",
"POMC neurons==>Astro_LXN+",
"AGRP/NPY neurons==>Astro_LXN+",
"POMC neurons==>Astro_LXN-",
"AGRP/NPY neurons==>Astro_LXN-"
),
name = ""
) +
theme_combmatrix(
combmatrix.label.text = element_text(size = 12),
combmatrix.label.extra_spacing = 5
)
df_complex_conditions2 |>
filter(pairs %in% c(
"Astro_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>AGRP/NPY neurons"
# "POMC neurons==>Astro_LXN+",
# "AGRP/NPY neurons==>Astro_LXN+",
# "POMC neurons==>Astro_LXN-",
# "AGRP/NPY neurons==>Astro_LXN-"
),
position = "top",
name = ""
) +
theme_combmatrix(
combmatrix.label.text = element_text(size = 12),
combmatrix.label.extra_spacing = 5
)
df_details <- df_complex_conditions |>
filter(pairs %in% c(
"Astro_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
color = 'blue'
),
upset_query(
group = "Astro_LXN-==>POMC neurons",
color = 'orange'
),
upset_query(
group = "Astro_LXN+==>AGRP/NPY neurons",
color = 'purple'
),
upset_query(
group = "Astro_LXN-==>AGRP/NPY neurons",
color = 'cyan'
),
upset_query(
set = "Astro_LXN+==>POMC neurons",
fill = 'blue'
),
upset_query(
set = "Astro_LXN-==>POMC neurons",
fill = 'orange'
),
upset_query(
set = "Astro_LXN+==>AGRP/NPY neurons",
fill = 'purple'
),
upset_query(
set = "Astro_LXN-==>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')
)
)
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_LXN+==>POMC neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons"
),
paired = FALSE,
id.column = "...1"
)
two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiffdabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:44 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_specificity_rank
Unpaired mean difference of Astro_LXN-==>POMC neurons (n = 375) minus Astro_LXN+==>POMC neurons (n = 458)
-0.195 [95CI -0.342; -0.0576]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.meandiff,
slopegraph = FALSE, color.column = control)
two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_ddabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:44 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_specificity_rank
Unpaired Cohen's d of Astro_LXN-==>POMC neurons (n = 375) minus Astro_LXN+==>POMC neurons (n = 458)
-0.184 [95CI -0.327; -0.0546]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.cohens_d,
slopegraph = FALSE, color.column = control)
df.multi.group <-
df_complex_conditions |>
select(
pairs,
ligand_complex,
receptor_complex,
region,
condition,
control,
specificity_rank
) |>
filter(
region == "ARC",
pairs %in% c(
"Astro_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"Norm: Astro_LXN-==>POMC neurons",
"Norm: Astro_LXN+==>AGRP/NPY neurons",
"Norm: Astro_LXN-==>AGRP/NPY neurons"
),
c(
"HFD: Astro_LXN+==>POMC neurons",
"HFD: Astro_LXN-==>POMC neurons",
"HFD: Astro_LXN+==>AGRP/NPY neurons",
"HFD: Astro_LXN-==>AGRP/NPY neurons"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff, color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d, color.column = condition)
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_LXN+==>POMC neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons"
),
paired = FALSE,
id.column = "...1"
)
two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiffdabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:45 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_magnitude_rank
Unpaired mean difference of Astro_LXN-==>POMC neurons (n = 375) minus Astro_LXN+==>POMC neurons (n = 458)
0.0946 [95CI -0.0866; 0.28]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.meandiff,
slopegraph = FALSE, color.column = control)
two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_ddabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:45 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_magnitude_rank
Unpaired Cohen's d of Astro_LXN-==>POMC neurons (n = 375) minus Astro_LXN+==>POMC neurons (n = 458)
0.0704 [95CI -0.0676; 0.204]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.cohens_d,
slopegraph = FALSE, color.column = control)
df.multi.group <-
df_complex_conditions |>
select(
pairs,
ligand_complex,
receptor_complex,
region,
condition,
control,
magnitude_rank
) |>
filter(
region == "ARC",
pairs %in% c(
"Astro_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"Norm: Astro_LXN-==>POMC neurons",
"Norm: Astro_LXN+==>AGRP/NPY neurons",
"Norm: Astro_LXN-==>AGRP/NPY neurons"
),
c(
"HFD: Astro_LXN+==>POMC neurons",
"HFD: Astro_LXN-==>POMC neurons",
"HFD: Astro_LXN+==>AGRP/NPY neurons",
"HFD: Astro_LXN-==>AGRP/NPY neurons"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
color.column = condition)
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_LXN+==>POMC neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons"
),
paired = FALSE,
id.column = "...1"
)
two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiffdabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:46 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_specificity_rank
Unpaired mean difference of Astro_LXN-==>POMC neurons (n = 138) minus Astro_LXN+==>POMC neurons (n = 215)
-0.235 [95CI -0.429; -0.0304]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.meandiff,
slopegraph = FALSE, color.column = control)
two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_ddabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:46 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_specificity_rank
Unpaired Cohen's d of Astro_LXN-==>POMC neurons (n = 138) minus Astro_LXN+==>POMC neurons (n = 215)
-0.247 [95CI -0.452; -0.0271]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.cohens_d,
slopegraph = FALSE, color.column = control)
df.multi.group <-
df_complex_conditions |>
select(
pairs,
ligand_complex,
receptor_complex,
region,
condition,
control,
specificity_rank
) |>
filter(
region == "PVN",
pairs %in% c(
"Astro_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"Norm: Astro_LXN-==>POMC neurons",
"Norm: Astro_LXN+==>AGRP/NPY neurons",
"Norm: Astro_LXN-==>AGRP/NPY neurons"
),
c(
"CSDS: Astro_LXN+==>POMC neurons",
"CSDS: Astro_LXN-==>POMC neurons",
"CSDS: Astro_LXN+==>AGRP/NPY neurons",
"CSDS: Astro_LXN-==>AGRP/NPY neurons"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d)
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_LXN+==>POMC neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons"
),
paired = FALSE,
id.column = "...1"
)
two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiffdabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:46 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_magnitude_rank
Unpaired mean difference of Astro_LXN-==>POMC neurons (n = 138) minus Astro_LXN+==>POMC neurons (n = 215)
0.259 [95CI -0.0335; 0.575]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.meandiff,
slopegraph = FALSE, color.column = control)
two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_ddabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:46 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_magnitude_rank
Unpaired Cohen's d of Astro_LXN-==>POMC neurons (n = 138) minus Astro_LXN+==>POMC neurons (n = 215)
0.189 [95CI -0.0291; 0.405]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.cohens_d,
slopegraph = FALSE, color.column = control)
df.multi.group <-
df_complex_conditions |>
select(
pairs,
ligand_complex,
receptor_complex,
region,
condition,
control,
magnitude_rank
) |>
filter(
region == "PVN",
pairs %in% c(
"Astro_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"Norm: Astro_LXN-==>POMC neurons",
"Norm: Astro_LXN+==>AGRP/NPY neurons",
"Norm: Astro_LXN-==>AGRP/NPY neurons"
),
c(
"CSDS: Astro_LXN+==>POMC neurons",
"CSDS: Astro_LXN-==>POMC neurons",
"CSDS: Astro_LXN+==>AGRP/NPY neurons",
"CSDS: Astro_LXN-==>AGRP/NPY neurons"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d)
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_LXN+",
"POMC neurons==>Astro_LXN-"
)) |>
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_LXN+",
"POMC neurons==>Astro_LXN-"
),
paired = FALSE,
id.column = "...1"
)
two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiffdabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:46 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_specificity_rank
Unpaired mean difference of POMC neurons==>Astro_LXN- (n = 462) minus POMC neurons==>Astro_LXN+ (n = 501)
-0.22 [95CI -0.357; -0.0769]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.meandiff,
slopegraph = FALSE, color.column = control)
two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_ddabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:47 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_specificity_rank
Unpaired Cohen's d of POMC neurons==>Astro_LXN- (n = 462) minus POMC neurons==>Astro_LXN+ (n = 501)
-0.193 [95CI -0.314; -0.0672]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.cohens_d,
slopegraph = FALSE, color.column = control)
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_LXN+",
"POMC neurons==>Astro_LXN-",
"AGRP/NPY neurons==>Astro_LXN+",
"AGRP/NPY neurons==>Astro_LXN-"
)) |>
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_LXN+",
"Norm: POMC neurons==>Astro_LXN-",
"Norm: AGRP/NPY neurons==>Astro_LXN+",
"Norm: AGRP/NPY neurons==>Astro_LXN-"
),
c(
"HFD: POMC neurons==>Astro_LXN+",
"HFD: POMC neurons==>Astro_LXN-",
"HFD: AGRP/NPY neurons==>Astro_LXN+",
"HFD: AGRP/NPY neurons==>Astro_LXN-"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff, color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d, color.column = condition)
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_LXN+",
"POMC neurons==>Astro_LXN-"
)) |>
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_LXN+",
"POMC neurons==>Astro_LXN-"
),
paired = FALSE,
id.column = "...1"
)
two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiffdabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:48 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_magnitude_rank
Unpaired mean difference of POMC neurons==>Astro_LXN- (n = 462) minus POMC neurons==>Astro_LXN+ (n = 501)
-0.00637 [95CI -0.155; 0.137]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.meandiff,
slopegraph = FALSE, color.column = control)
two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_ddabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:48 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_magnitude_rank
Unpaired Cohen's d of POMC neurons==>Astro_LXN- (n = 462) minus POMC neurons==>Astro_LXN+ (n = 501)
-0.0055 [95CI -0.138; 0.117]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.cohens_d,
slopegraph = FALSE, color.column = control)
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_LXN+",
"POMC neurons==>Astro_LXN-",
"AGRP/NPY neurons==>Astro_LXN+",
"AGRP/NPY neurons==>Astro_LXN-"
)) |>
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_LXN+",
"Norm: POMC neurons==>Astro_LXN-",
"Norm: AGRP/NPY neurons==>Astro_LXN+",
"Norm: AGRP/NPY neurons==>Astro_LXN-"
),
c(
"HFD: POMC neurons==>Astro_LXN+",
"HFD: POMC neurons==>Astro_LXN-",
"HFD: AGRP/NPY neurons==>Astro_LXN+",
"HFD: AGRP/NPY neurons==>Astro_LXN-"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
color.column = condition)
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_LXN+",
"POMC neurons==>Astro_LXN-"
)) |>
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_LXN+",
"POMC neurons==>Astro_LXN-"
),
paired = FALSE,
id.column = "...1"
)
two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiffdabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:49 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_specificity_rank
Unpaired mean difference of POMC neurons==>Astro_LXN- (n = 172) minus POMC neurons==>Astro_LXN+ (n = 219)
-0.328 [95CI -0.542; -0.116]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.meandiff,
slopegraph = FALSE, color.column = control)
two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_ddabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:49 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_specificity_rank
Unpaired Cohen's d of POMC neurons==>Astro_LXN- (n = 172) minus POMC neurons==>Astro_LXN+ (n = 219)
-0.306 [95CI -0.51; -0.107]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.cohens_d,
slopegraph = FALSE, color.column = control)
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_LXN+",
"POMC neurons==>Astro_LXN-",
"AGRP/NPY neurons==>Astro_LXN+",
"AGRP/NPY neurons==>Astro_LXN-"
)) |>
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_LXN+",
"Norm: POMC neurons==>Astro_LXN-",
"Norm: AGRP/NPY neurons==>Astro_LXN+",
"Norm: AGRP/NPY neurons==>Astro_LXN-"
),
c(
"CSDS: POMC neurons==>Astro_LXN+",
"CSDS: POMC neurons==>Astro_LXN-",
"CSDS: AGRP/NPY neurons==>Astro_LXN+",
"CSDS: AGRP/NPY neurons==>Astro_LXN-"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d)
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_LXN+",
"POMC neurons==>Astro_LXN-"
)) |>
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_LXN+",
"POMC neurons==>Astro_LXN-"
),
paired = FALSE,
id.column = "...1"
)
two.group.unpaired.meandiff <- mean_diff(two.group.unpaired)
two.group.unpaired.meandiffdabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:49 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_magnitude_rank
Unpaired mean difference of POMC neurons==>Astro_LXN- (n = 172) minus POMC neurons==>Astro_LXN+ (n = 219)
-0.029 [95CI -0.274; 0.238]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.meandiff,
slopegraph = FALSE, color.column = control)
two.group.unpaired.cohens_d <- cohens_d(two.group.unpaired)
two.group.unpaired.cohens_ddabestr (Data Analysis with Bootstrap Estimation in R) v0.3.0
=============================================================
Good evening!
The current time is 20:49 PM on Wednesday May 17, 2023.
Dataset : df.two.group.unpaired
X Variable : pairs
Y Variable : neg_log10_magnitude_rank
Unpaired Cohen's d of POMC neurons==>Astro_LXN- (n = 172) minus POMC neurons==>Astro_LXN+ (n = 219)
-0.0234 [95CI -0.231; 0.182]
5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.plot(two.group.unpaired.cohens_d,
slopegraph = FALSE, color.column = control)
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_LXN+",
"POMC neurons==>Astro_LXN-",
"AGRP/NPY neurons==>Astro_LXN+",
"AGRP/NPY neurons==>Astro_LXN-"
)) |>
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_LXN+",
"Norm: POMC neurons==>Astro_LXN-",
"Norm: AGRP/NPY neurons==>Astro_LXN+",
"Norm: AGRP/NPY neurons==>Astro_LXN-"
),
c(
"CSDS: POMC neurons==>Astro_LXN+",
"CSDS: POMC neurons==>Astro_LXN-",
"CSDS: AGRP/NPY neurons==>Astro_LXN+",
"CSDS: AGRP/NPY neurons==>Astro_LXN-"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d)
df.multi.group <-
df_complex_conditions |>
select(
pairs,
ligand_complex,
receptor_complex,
region,
condition,
control,
specificity_rank
) |>
filter(
!control,
pairs %in% c(
"Astro_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"ARC: Astro_LXN-==>POMC neurons",
"PVN: Astro_LXN+==>POMC neurons",
"PVN: Astro_LXN-==>POMC neurons"
),
c(
"ARC: Astro_LXN+==>AGRP/NPY neurons",
"ARC: Astro_LXN-==>AGRP/NPY neurons",
"PVN: Astro_LXN+==>AGRP/NPY neurons",
"PVN: Astro_LXN-==>AGRP/NPY neurons"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
color.column = condition)
df.multi.group <-
df_complex_conditions |>
select(
pairs,
ligand_complex,
receptor_complex,
region,
condition,
control,
specificity_rank
) |>
filter(
control,
pairs %in% c(
"Astro_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"ARC: Astro_LXN-==>POMC neurons",
"PVN: Astro_LXN+==>POMC neurons",
"PVN: Astro_LXN-==>POMC neurons"
),
c(
"ARC: Astro_LXN+==>AGRP/NPY neurons",
"ARC: Astro_LXN-==>AGRP/NPY neurons",
"PVN: Astro_LXN+==>AGRP/NPY neurons",
"PVN: Astro_LXN-==>AGRP/NPY neurons"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
color.column = condition)
df.multi.group <-
df_complex_conditions |>
select(
pairs,
ligand_complex,
receptor_complex,
region,
condition,
control,
magnitude_rank
) |>
filter(
!control,
pairs %in% c(
"Astro_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"ARC: Astro_LXN-==>POMC neurons",
"PVN: Astro_LXN+==>POMC neurons",
"PVN: Astro_LXN-==>POMC neurons"
),
c(
"ARC: Astro_LXN+==>AGRP/NPY neurons",
"ARC: Astro_LXN-==>AGRP/NPY neurons",
"PVN: Astro_LXN+==>AGRP/NPY neurons",
"PVN: Astro_LXN-==>AGRP/NPY neurons"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
color.column = condition)
df.multi.group <-
df_complex_conditions |>
select(
pairs,
ligand_complex,
receptor_complex,
region,
condition,
control,
magnitude_rank
) |>
filter(
control,
pairs %in% c(
"Astro_LXN+==>POMC neurons",
"Astro_LXN-==>POMC neurons",
"Astro_LXN+==>AGRP/NPY neurons",
"Astro_LXN-==>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_LXN+==>POMC neurons",
"ARC: Astro_LXN-==>POMC neurons",
"PVN: Astro_LXN+==>POMC neurons",
"PVN: Astro_LXN-==>POMC neurons"
),
c(
"ARC: Astro_LXN+==>AGRP/NPY neurons",
"ARC: Astro_LXN-==>AGRP/NPY neurons",
"PVN: Astro_LXN+==>AGRP/NPY neurons",
"PVN: Astro_LXN-==>AGRP/NPY neurons"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
color.column = condition)
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_LXN+",
"POMC neurons==>Astro_LXN-",
"AGRP/NPY neurons==>Astro_LXN+",
"AGRP/NPY neurons==>Astro_LXN-"
)) |>
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_LXN+",
"ARC: POMC neurons==>Astro_LXN-",
"PVN: POMC neurons==>Astro_LXN+",
"PVN: POMC neurons==>Astro_LXN-"
),
c(
"ARC: AGRP/NPY neurons==>Astro_LXN+",
"ARC: AGRP/NPY neurons==>Astro_LXN-",
"PVN: AGRP/NPY neurons==>Astro_LXN+",
"PVN: AGRP/NPY neurons==>Astro_LXN-"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
color.column = condition)
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_LXN+",
"POMC neurons==>Astro_LXN-",
"AGRP/NPY neurons==>Astro_LXN+",
"AGRP/NPY neurons==>Astro_LXN-"
)) |>
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_LXN+",
"ARC: POMC neurons==>Astro_LXN-",
"PVN: POMC neurons==>Astro_LXN+",
"PVN: POMC neurons==>Astro_LXN-"
),
c(
"ARC: AGRP/NPY neurons==>Astro_LXN+",
"ARC: AGRP/NPY neurons==>Astro_LXN-",
"PVN: AGRP/NPY neurons==>Astro_LXN+",
"PVN: AGRP/NPY neurons==>Astro_LXN-"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
color.column = condition)
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_LXN+",
"POMC neurons==>Astro_LXN-",
"AGRP/NPY neurons==>Astro_LXN+",
"AGRP/NPY neurons==>Astro_LXN-"
)) |>
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_LXN+",
"ARC: POMC neurons==>Astro_LXN-",
"PVN: POMC neurons==>Astro_LXN+",
"PVN: POMC neurons==>Astro_LXN-"
),
c(
"ARC: AGRP/NPY neurons==>Astro_LXN+",
"ARC: AGRP/NPY neurons==>Astro_LXN-",
"PVN: AGRP/NPY neurons==>Astro_LXN+",
"PVN: AGRP/NPY neurons==>Astro_LXN-"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
color.column = condition)
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_LXN+",
"POMC neurons==>Astro_LXN-",
"AGRP/NPY neurons==>Astro_LXN+",
"AGRP/NPY neurons==>Astro_LXN-"
)) |>
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_LXN+",
"ARC: POMC neurons==>Astro_LXN-",
"PVN: POMC neurons==>Astro_LXN+",
"PVN: POMC neurons==>Astro_LXN-"
),
c(
"ARC: AGRP/NPY neurons==>Astro_LXN+",
"ARC: AGRP/NPY neurons==>Astro_LXN-",
"PVN: AGRP/NPY neurons==>Astro_LXN+",
"PVN: AGRP/NPY neurons==>Astro_LXN-"
)
),
paired = FALSE,
id.column = "signal"
)
multi.group.mean_diff <- multi.group %>% mean_diff()
plot(multi.group.mean_diff,
color.column = condition)
multi.group.cohens_d <- multi.group %>% cohens_d()
plot(multi.group.cohens_d,
color.column = condition)
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.2.3 (2023-03-15)
os macOS Ventura 13.3.1
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Vienna
date 2023-05-17
pandoc 2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (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)
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)
rstudioapi 0.14 2022-08-22 [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
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