# Load tidyverse infrastructure packages
suppressPackageStartupMessages({
library(future)
library(here)
library(tidyverse)
library(magrittr)
library(stringr)
library(skimr)
library(RColorBrewer)
library(viridis)
})
# Load packages for scRNA-seq analysis and visualisation
suppressPackageStartupMessages({
library(UpSetR)
library(ggplot2)
library(cowplot)
library(patchwork)
library(ggstatsplot)
library(Seurat)
library(SeuratWrappers)
library(scCustomize)
})src_dir <- here("code")
data_dir <- here("data")
output_dir <- here("output")source(here(src_dir, "genes.R"))# set seed
reseed <- 42
set.seed(seed = reseed)
# Parameters for parallel execution
n_cores <- min(parallelly::availableCores() / 2 - 1, 16)
plan("multisession", workers = n_cores)
options(
future.globals.maxSize = 100000 * 1024^2,
future.rng.onMisuse = "ignore"
)
plan()multisession:
- args: function (..., workers = 16, envir = parent.frame())
- tweaked: TRUE
- call: plan("multisession", workers = n_cores)# ggplot2 theme
theme_set(ggmin::theme_powerpoint())bioproject <- "PRJNA547712"
project <- "kim2020_Hypoth-dev"
cb_fpr <- 0.001
low_cutoff_gene <- 500
high_cutoff_gene <- NULL
high_cutoff_gene <- 5000
low_cutoff_umis <- NULL
low_cutoff_umis <- -Inf
high_cutoff_umis <- 25000
high_cutoff_pc_mt <- 15
high_cutoff_pc_ribo <- 20
high_cutoff_pc_hb <- 0.1
high_cutoff_doublet_score <- 0.33
high_cutoff_complexity <- 0.85
connectivity_model <- "min_tree"
k <- 10
metric <- "euclidean"
signature <- 100opioid_system_genes <- c(
# Classical opioid receptors
"Oprd1", # Delta opioid receptor
"Oprk1", # Kappa opioid receptor
"Oprl1", # Nociceptin/orphanin FQ receptor
"Oprm1", # Mu opioid receptor
# Processing enzymes
"Pcsk1", # Proprotein convertase 1
"Pcsk2", # Proprotein convertase 2
# Endogenous opioid precursors
"Pdyn", # Prodynorphin
"Penk", # Proenkephalin
# "Pomc", # Proopiomelanocortin
"Pnoc" # Prepronociceptin
)metabolic_signaling_genes <- c(
# Receptor tyrosine kinases and ligands
"Alk", # Anaplastic lymphoma kinase - neural development, metabolism
"Fam150a", # ALK ligand 1/Augmentor-β - ALK receptor activator
"Fam150b", # ALK ligand 2/Augmentor-α - ALK receptor activator
# Melanocortin system
"Mc3r", # Melanocortin 3 receptor - energy homeostasis, inflammation
"Mc4r", # Melanocortin 4 receptor - appetite control, energy balance
# Metabolic hormone receptors
"Lepr", # Leptin receptor - energy balance, satiety
"Insr", # Insulin receptor - glucose homeostasis
# "Igf1r", # Insulin-like growth factor 1 receptor - growth, development
# Signaling adaptors/regulators
"Lmo4", # LIM domain only 4 - transcriptional regulation, metabolism
"Irs1", # Insulin receptor substrate 1 - insulin signaling
"Irs4" # Insulin receptor substrate 4 - insulin/leptin signaling
)srt.kim <- schard::h5ad2seurat(
here(
data_dir,
"kim2020_combined.h5ad"
),
use.raw = TRUE
)
X_umap <- srt.kim@meta.data |>
dplyr::select(X, Y, Z) |>
as.matrix()
colnames(X_umap) <- c("UMAP_1", "UMAP_2", "UMAP_3")
rownames(X_umap) <- colnames(srt.kim)
srt.kim[["umap"]] <- CreateDimReducObject(
embeddings = X_umap,
key = "umap_",
assay = DefaultAssay(srt.kim)
)
srt.kim$Age %<>%
forcats::fct(
levels = c(
"E10",
"E11",
"E12",
"E13",
"E14",
"E15",
"E16",
"E17",
"E18",
"P0",
"P2",
"P4",
"P8",
"P10",
"P14",
"P23",
"P45"
)
)
Idents(srt.kim) <- "Age"
srt.kim <- Store_Palette_Seurat(
seurat_object = srt.kim,
palette = rev(brewer.pal(n = 11, name = "Spectral")),
palette_name = "expr_Colour_Pal"
)print(srt.kim)An object of class Seurat
27998 features across 128006 samples within 1 assay
Active assay: RNA (27998 features, 0 variable features)
2 layers present: counts, data
1 dimensional reduction calculated: umapsrt.romanov.pub <- readRDS("/data/PRJNA548917/old/oldCCA_nae_srt.rds")
srt.romanov.pub <- UpdateSeuratObject(srt.romanov.pub)
Idents(srt.romanov.pub) <-
factor(srt.romanov.pub$wtree, ordered = TRUE)
# Consistent colours and clusters names
colours_wtree <- setNames(read_lines(here(data_dir, "colours_wtree.tsv")), 1:45)
srt.romanov.pub$age <-
Cells(srt.romanov.pub) |>
str_split(pattern = ":", simplify = T) %>%
.[, 1] %>%
str_split_fixed(pattern = "_", n = 3) %>%
.[, 3]
print(srt.romanov.pub)An object of class Seurat
24340 features across 51199 samples within 1 assay
Active assay: RNA (24340 features, 3500 variable features)
3 layers present: counts, data, scale.data
3 dimensional reductions calculated: pca, tsne, umapglimpse(srt.romanov.pub@meta.data)Rows: 51,199
Columns: 20
$ nGene <int> 1652, 782, 447, 1706, 1106, 894, 727, 734, 669, 617, …
$ nUMI <dbl> 2787, 1090, 544, 2709, 1817, 1220, 995, 1036, 920, 86…
$ orig.ident <fct> Hypothalamus, Hypothalamus, Hypothalamus, Hypothalamu…
$ res.0.2 <chr> "23", "23", "23", "23", "23", "23", "23", "23", "23",…
$ res.0.4 <chr> "34", "34", "34", "34", "34", "34", "34", "34", "34",…
$ res.0.8 <chr> "42", "42", "42", "42", "42", "42", "42", "42", "42",…
$ res.1.2 <chr> "47", "47", "47", "47", "47", "47", "47", "47", "47",…
$ res.2 <chr> "54", "54", "54", "54", "54", "54", "54", "54", "54",…
$ tree.ident <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ pro_Inter <chr> "41", "41", "41", "41", "41", "41", "41", "41", "41",…
$ pro_Enter <chr> "41", "41", "41", "41", "41", "41", "41", "41", "41",…
$ tree_final <fct> 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 1…
$ subtree <fct> 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 4…
$ prim_walktrap <fct> 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3…
$ umi_per_gene <dbl> 1.687046, 1.393862, 1.217002, 1.587925, 1.642857, 1.3…
$ log_umi_per_gene <dbl> 0.22712693, 0.14421974, 0.08529138, 0.20082998, 0.215…
$ nCount_RNA <dbl> 2787, 1090, 544, 2709, 1817, 1220, 995, 1036, 920, 86…
$ nFeature_RNA <int> 1652, 782, 447, 1706, 1106, 894, 727, 734, 669, 617, …
$ wtree <fct> 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3…
$ age <chr> "P23", "3P2", "3P2", "P2", "P2", "P2", "P2", "P2", "P…table(Idents(srt.romanov.pub))
1 2 3 4 5 6 7 8 9 10 11 12 13
2344 8146 395 402 3234 712 552 374 259 952 13727 1615 765
14 15 16 17 18 19 20 21 22 23 24 25 26
832 1244 792 590 808 2486 1683 628 1039 1750 292 394 547
27 28 29 30 31 32 33 34 35 36 37 38 39
391 407 507 93 81 402 143 701 222 353 324 73 78
40 41 42 43 44 45
328 190 73 37 179 55 srt.romanov.pub %<>% RenameIdents(object = ., `43` = "mneOXY")
srt.romanov.pub %<>% RenameIdents(object = ., `26` = "mneVAS")
srt.romanov.pub %<>% RenameIdents(object = ., `31` = "pneSS")
srt.romanov.pub %<>% RenameIdents(object = ., `24` = "pneCRH")
srt.romanov.pub %<>% RenameIdents(object = ., `15` = "pneTRH")srt.romanov.pub$stage <-
srt.romanov.pub$age %>%
forcats::fct_collapse(
Embryonic = c("E15", "E17"),
Neonatal = c("P0", "P2", "3P2"),
Pubertal = c("1P10", "P10"),
Adult = c("P23")
)
srt.romanov.pub$stage %<>%
factor(
levels = c("Embryonic", "Neonatal", "Pubertal", "Adult"),
ordered = TRUE
)
srt.romanov.pub$stage %>% forcats::fct_count()srt.romanov.pub$age <-
plyr::mapvalues(
x = srt.romanov.pub$age,
from = c("E15", "E17", "P0", "P2", "3P2", "1P10", "P10", "P23"),
to = c("E15", "E17", "P00", "P02", "P02", "P10", "P10", "P23")
)
srt.romanov.pub$age %>% forcats::fct_count()srt.kim <- NormalizeData(srt.kim)
srt.kim <- FindVariableFeatures(
srt.kim,
selection.method = "vst",
nfeatures = 3000
)
# all.genes <- rownames(srt.kim)
# srt.kim <- ScaleData(srt.kim, features = all.genes)
srt.kim <- ScaleData(srt.kim)hypoth.anchors <- FindTransferAnchors(
reference = srt.romanov.pub,
query = srt.kim,
dims = 1:30,
reference.reduction = "pca"
)
predictions <- TransferData(
anchorset = hypoth.anchors,
refdata = srt.romanov.pub$wtree,
dims = 1:30
)
srt.kim <- AddMetaData(srt.kim, metadata = predictions)
table(srt.kim$predicted.id)
1 10 11 12 13 15 16 17 19 2 20
3700 1 2198 225 545 58 218 19 100308 9886 924
22 23 26 27 28 29 3 38 4 5 6
163 11 115 24 76 481 951 49 156 2895 4756
7 8 9
199 47 1 srt.romanov.pub <- RunUMAP(
srt.romanov.pub,
dims = 1:30,
reduction = "pca",
return.model = TRUE
)
srt.kim <- IntegrateEmbeddings(
anchorset = hypoth.anchors,
reference = srt.romanov.pub,
query = srt.kim,
new.reduction.name = "ref.pca"
)
srt.kim <- ProjectUMAP(
query = srt.kim,
query.reduction = "ref.pca",
reference = srt.romanov.pub,
reference.reduction = "pca",
reduction.model = "umap"
)
Idents(srt.kim) <- srt.kim$Clusterall.genes <- rownames(srt.kim)
gene.scale <- c(
cnbn,
opioid_system_genes,
metabolic_signaling_genes,
np,
npr,
nmr,
neurotrans
) |>
unique() %>%
.[. %in% all.genes]srt.kim$stage <-
srt.kim$Age %>%
forcats::fct_collapse(
Embryonic = c(
"E10",
"E11",
"E12",
"E13",
"E14",
"E15",
"E16",
"E18"
),
Neonatal = c("P4", "P8"),
Pubertal = c("P14"),
Adult = c("P45")
)
srt.kim$stage %<>%
factor(
levels = c("Embryonic", "Neonatal", "Pubertal", "Adult"),
ordered = TRUE
)selected_cells <- read_lines(here(data_dir, "kim2020_pvn_neurons.txt"))
srt.kim <- subset(
srt.kim,
cells = c(
selected_cells,
WhichCells(
srt.kim,
expression = (Crh > 0 & (Scgn > 0 | Alk > 0 | Fam150b > 0 | Fam150a > 0))
)
)
)
# srt.kim <- subset(srt.kim, subset = refUMAP_1 > 4 & refUMAP_2 > -1)
srt.kim@meta.data <- srt.kim@meta.data |>
dplyr::rename(wtree = predicted.id, age = Age)
srt.kim <- subset(srt.kim, subset = stage %in% c("Pubertal", "Adult"))
srt.kimAn object of class Seurat
27998 features across 954 samples within 1 assay
Active assay: RNA (27998 features, 3000 variable features)
3 layers present: counts, data, scale.data
3 dimensional reductions calculated: umap, ref.pca, ref.umapWe subset Kim et al., 2020 dataset to only Pubertal and Adult stages.
sbs_mtx <-
srt.kim@assays$RNA@data %>%
as.data.frame() %>%
t()
rownames(sbs_mtx) <- colnames(srt.kim)
colnames(sbs_mtx) <- rownames(srt.kim)
# Filter features
filt_low_genes <-
colSums(sbs_mtx) %>%
.[. > quantile(., 0.4)] %>%
names()
sbs_mtx %<>% .[, filt_low_genes]
min_filt_vector2 <-
sbs_mtx %>%
as_tibble() %>%
dplyr::select(all_of(filt_low_genes)) %>%
summarise(across(.fns = ~ quantile(.x, .005))) %>%
as.list() %>%
map(as.double) %>%
simplify() %>%
.[filt_low_genes]
# Prepare table of intersection sets analysis
content_sbs_mtx_kim <-
(sbs_mtx > min_filt_vector2) %>%
as_tibble() %>%
mutate_all(as.numeric) %>%
bind_cols(
srt.kim@meta.data |> dplyr::select(wtree, age, stage)
)srt.romanov.pvn <-
subset(
x = srt.romanov.pub,
cells = unique(c(
WhichCells(
srt.romanov.pub,
idents = c(
"mneOXY",
"mneVAS",
"pneSS",
"pneCRH",
"pneTRH"
)
),
WhichCells(
srt.romanov.pub,
expression = (Crh > 0 &
(Scgn > 0 | Alk > 0 | Fam150b > 0 | Fam150a > 0))
)
)),
invert = FALSE
)
table(srt.romanov.pvn$age)
E15 E17 P00 P02 P10 P23
332 615 374 389 537 67 table(srt.romanov.pvn$stage)
Embryonic Neonatal Pubertal Adult
947 763 537 67 srt.romanov.pvn <- subset(
srt.romanov.pvn,
subset = stage %in% c("Pubertal", "Adult")
)We subset Romanov et al., 2020 dataset to only Pubertal and Adult stages.
table(srt.romanov.pvn$age)
P10 P23
537 67 table(srt.romanov.pvn$stage)
Embryonic Neonatal Pubertal Adult
0 0 537 67 sbs_mtx <-
srt.romanov.pvn@assays$RNA@data %>%
as.data.frame() %>%
t()
# Filter features
filt_low_genes <-
colSums(sbs_mtx) %>%
.[. > quantile(., 0.4)] %>%
names()
sbs_mtx %<>% .[, c("Fam150b", filt_low_genes)]
min_filt_vector2 <-
sbs_mtx %>%
as_tibble() %>%
dplyr::select(all_of(c("Fam150b", filt_low_genes))) %>%
summarise(across(.fns = ~ quantile(.x, .005))) %>%
as.list() %>%
map(as.double) %>%
simplify() %>%
.[c("Fam150b", filt_low_genes)]
# Prepare table of intersection sets analysis
content_sbs_mtx_romanov <-
(sbs_mtx > min_filt_vector2) %>%
as_tibble() %>%
mutate_all(as.numeric) %>%
bind_cols(
srt.romanov.pvn@meta.data |> dplyr::select(wtree, age, stage)
)# Prepare table of intersection sets analysis
to_select <-
c(gene.scale, "wtree", "age", "stage") %>%
.[. %in% colnames(content_sbs_mtx_kim)] %>%
.[. %in% colnames(content_sbs_mtx_romanov)]
content_sbs_mtx <-
bind_rows(
content_sbs_mtx_kim |> dplyr::select(all_of(to_select)),
content_sbs_mtx_romanov |> dplyr::select(all_of(to_select))
)metabolic_signaling_genes <- c(
"Alk",
"Fam150a",
"Fam150b",
"Mc3r",
"Mc4r",
"Lepr",
"Insr",
"Lmo4",
"Irs1",
"Irs4"
)
# 1. Prepare the data for the UpSet plot (specifically for Pubertal stage)
data_for_upset_pubertal <- content_sbs_mtx |>
dplyr::filter(stage == "Pubertal") |>
# Select the genes of interest, including Fam150b via metabolic_signaling_genes
dplyr::select(dplyr::any_of(c(
metabolic_signaling_genes,
"Crh",
"Trh",
"Oxt"
))) |>
# IMPORTANT: This step removes any gene column that has no expressing cells in this subset
dplyr::select(dplyr::where(~ is.numeric(.x) && sum(.x, na.rm = TRUE) > 0))
# Convert to a standard data frame for UpSetR
data_for_upset_pubertal_df <- as.data.frame(data_for_upset_pubertal)
# 2. Check if Fam150b is present after filtering
if ("Fam150b" %in% colnames(data_for_upset_pubertal_df)) {
cat("Fam150b is present in the data for the UpSet plot.\n")
cat(
"Number of cells expressing Fam150b in Young mice hypothalamic neurons (after binarization):",
sum(data_for_upset_pubertal_df$Fam150b, na.rm = TRUE),
"\n"
)
} else {
cat("Fam150b is NOT present in the data for the UpSet plot.\n")
cat(
"This means no cells in the Young stage expressed Fam150b. \n"
)
# Optional: Check Fam150b in the original Pubertal subset before the sum filter
pubertal_subset_before_sum_filter <- content_sbs_mtx |>
dplyr::filter(stage == "Pubertal") |>
dplyr::select(dplyr::any_of("Fam150b"))
if ("Fam150b" %in% colnames(pubertal_subset_before_sum_filter)) {
cat(
"Original expression of Fam150b in Young mice hypothalamic neurons (sum after binarization):",
sum(pubertal_subset_before_sum_filter$Fam150b, na.rm = TRUE),
"\n"
)
} else {
cat(
"Fam150b was not even found in the selection from content_sbs_mtx for Young mice hypothalamic neurons.\n"
)
}
}Fam150b is present in the data for the UpSet plot.
Number of cells expressing Fam150b in Young mice hypothalamic neurons (after binarization): 5 # 3. Generate the UpSet plot using the prepared and filtered data
# The 'sets' argument should dynamically get column names from the prepared data.
if (
ncol(data_for_upset_pubertal_df) > 0 && nrow(data_for_upset_pubertal_df) > 0
) {
upset(
data_for_upset_pubertal_df,
# --- Ordering and Filtering ---
order.by = "freq", # Order intersections by frequency (size)
# keep.order = TRUE, # If you want to maintain the order of sets as in 'sets' argument
nsets = 30, # Max number of sets to display in the matrix
nintersects = 100, # Max number of intersections to display
# cutoff = 1, # Minimum size of an intersection to be shown
# --- Aesthetics: Colors and Points/Lines ---
main.bar.color = "gray20", # Color of the main intersection size bars
sets.bar.color = "black", # Color of the set size bars on the left
matrix.color = "red", # Color of the lines connecting the dots in the matrix
matrix.dot.alpha = 0.5, # Transparency of the dots (0=transparent, 1=opaque)
# The example shows solid dots, so alpha close to 1.
# Note: matrix.color might also affect dot color if specific dot color param is not available or used.
# UpsetR's parameters can sometimes be a bit tricky.
shade.color = "gray80", # Color of the shading for row highlighting (if enabled)
shade.alpha = 0.25, # Transparency of the shading
# --- Aesthetics: Points and Lines ---
point.size = 10.0, # Size of the dots in the matrix (adjust as needed)
line.size = 4.0, # Thickness of the lines in the matrix (adjust as needed)
# --- Text and Labels ---
sets.x.label = "Number of cells", # Label for the set size bars
mainbar.y.label = "Intersection size", # Label for the main intersection size bars
text.scale = c(
intersection_size_title = 4.0, # Scales various text elements
intersection_size_tick_labels = 3.5,
set_size_title = 4.0,
set_size_tick_labels = 3.5,
set_names = 3.8, # Size of the set names (gene names)
numbers_above_bars = 3.0 # Size of numbers above intersection bars
),
number.angles = 0, # Angle of the numbers above intersection bars
# --- Matrix Configuration ---
show.numbers = "yes", # Show numbers above intersection bars ("no" to hide)
# --- Set Configuration ---
sets = colnames(data_for_upset_pubertal_df), # Explicitly define the sets from your data
empty.intersections = NULL,
# --- Layout Ratio ---
mb.ratio = c(0.70, 0.30) # Ratio height_of_main_bar_plot / height_of_matrix (manual default)
)
} else {
cat(
"The data frame for the UpSet plot is empty (either no rows or no columns after filtering).\n"
)
cat("Number of rows:", nrow(data_for_upset_pubertal_df), "\n")
cat("Number of columns:", ncol(data_for_upset_pubertal_df), "\n")
}
# 4. Display skim output for the data being plotted
print(skim(data_for_upset_pubertal_df))── Data Summary ────────────────────────
Values
Name data_for_upset_pubertal_d...
Number of rows 753
Number of columns 11
_______________________
Column type frequency:
numeric 11
________________________
Group variables None
── Variable type: numeric ──────────────────────────────────────────────────────
skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100
1 Alk 0 1 0.369 0.483 0 0 0 1 1
2 Fam150b 0 1 0.00664 0.0813 0 0 0 0 1
3 Mc3r 0 1 0.0146 0.120 0 0 0 0 1
4 Mc4r 0 1 0.0332 0.179 0 0 0 0 1
5 Lepr 0 1 0.0279 0.165 0 0 0 0 1
6 Lmo4 0 1 0.284 0.451 0 0 0 1 1
7 Irs1 0 1 0.0916 0.289 0 0 0 0 1
8 Irs4 0 1 0.284 0.451 0 0 0 1 1
9 Crh 0 1 0.161 0.367 0 0 0 0 1
10 Trh 0 1 0.325 0.469 0 0 0 1 1
11 Oxt 0 1 0.390 0.488 0 0 0 1 1
hist
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sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.2 (2024-10-31)
os Ubuntu 22.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Vienna
date 2025-05-06
pandoc 3.4 @ /data/1_heteroAstrocytes/.pixi/envs/default/bin/ (via rmarkdown)
quarto 1.6.41 @ /data/1_heteroAstrocytes/.pixi/envs/default/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-5 2016-07-21 [1] CRAN (R 4.4.1)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.4.1)
bayestestR 0.15.3 2025-04-28 [1] CRAN (R 4.4.3)
beeswarm 0.4.0 2021-06-01 [1] RSPM
BiocManager 1.30.25 2024-08-28 [1] CRAN (R 4.4.1)
bit 4.5.0.1 2024-12-03 [1] CRAN (R 4.4.2)
bit64 4.5.2 2024-09-22 [1] CRAN (R 4.4.1)
circlize 0.4.16 2024-02-20 [1] RSPM
cli 3.6.4 2025-02-13 [1] CRAN (R 4.4.2)
cluster 2.1.8 2024-12-11 [1] CRAN (R 4.4.2)
codetools 0.2-20 2024-03-31 [1] CRAN (R 4.4.1)
colorspace 2.1-1 2024-07-26 [1] CRAN (R 4.4.1)
correlation 0.8.7 2025-03-03 [1] CRAN (R 4.4.3)
cowplot * 1.1.3 2024-01-22 [1] CRAN (R 4.4.1)
crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.1)
data.table 1.17.0 2025-02-22 [1] CRAN (R 4.4.2)
datawizard 1.0.2 2025-03-24 [1] CRAN (R 4.4.3)
deldir 2.0-4 2024-02-28 [1] CRAN (R 4.4.1)
digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1)
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evaluate 1.0.3 2025-01-10 [1] CRAN (R 4.4.2)
farver 2.1.2 2024-05-13 [1] CRAN (R 4.4.1)
fastDummies 1.7.5 2025-01-20 [1] CRAN (R 4.4.2)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.1)
fitdistrplus 1.2-2 2025-01-07 [1] CRAN (R 4.4.2)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.4.1)
future * 1.34.0 2024-07-29 [1] CRAN (R 4.4.1)
future.apply 1.11.3 2024-10-27 [1] CRAN (R 4.4.2)
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ggbeeswarm 0.7.2 2023-04-29 [1] RSPM
ggmin 0.0.0.9000 2025-03-07 [1] Github (sjessa/ggmin@8ada274)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.1)
ggprism 1.0.5 2024-03-21 [1] RSPM
ggrastr 1.0.2 2023-06-01 [1] RSPM
ggrepel 0.9.6 2024-09-07 [1] CRAN (R 4.4.1)
ggridges 0.5.6 2024-01-23 [1] CRAN (R 4.4.1)
ggstatsplot * 0.13.0 2024-12-04 [1] CRAN (R 4.4.2)
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glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1)
goftest 1.2-3 2021-10-07 [1] CRAN (R 4.4.1)
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gtable 0.3.6 2024-10-25 [1] CRAN (R 4.4.1)
here * 1.0.1 2020-12-13 [1] CRAN (R 4.4.1)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.1)
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htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.1)
httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.1)
httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.1)
ica 1.0-3 2022-07-08 [1] CRAN (R 4.4.1)
igraph 2.0.3 2024-03-13 [1] CRAN (R 4.4.1)
insight 1.2.0 2025-04-22 [1] CRAN (R 4.4.3)
irlba 2.3.5.1 2022-10-03 [1] CRAN (R 4.4.1)
janitor 2.2.1 2024-12-22 [1] RSPM
jsonlite 1.9.1 2025-03-03 [1] CRAN (R 4.4.3)
KernSmooth 2.23-26 2025-01-01 [1] CRAN (R 4.4.2)
knitr 1.49 2024-11-08 [1] CRAN (R 4.4.1)
labeling 0.4.3 2023-08-29 [1] CRAN (R 4.4.1)
later 1.4.1 2024-11-27 [1] CRAN (R 4.4.2)
lattice 0.22-6 2024-03-20 [1] CRAN (R 4.4.1)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.4.1)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.1)
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lmtest 0.9-40 2022-03-21 [1] CRAN (R 4.4.1)
lubridate * 1.9.4 2024-12-08 [1] CRAN (R 4.4.2)
magrittr * 2.0.3 2022-03-30 [1] CRAN (R 4.4.1)
MASS 7.3-64 2025-01-04 [1] CRAN (R 4.4.2)
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matrixStats 1.5.0 2025-01-07 [1] CRAN (R 4.4.2)
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miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.4.1)
munsell 0.5.1 2024-04-01 [1] CRAN (R 4.4.1)
nlme 3.1-167 2025-01-27 [1] CRAN (R 4.4.2)
paletteer 1.6.0 2024-01-21 [1] CRAN (R 4.4.1)
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patchwork * 1.3.0 2024-09-16 [1] CRAN (R 4.4.1)
pbapply 1.7-2 2023-06-27 [1] CRAN (R 4.4.1)
pillar 1.10.1 2025-01-07 [1] CRAN (R 4.4.2)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.1)
plotly 4.10.4 2024-01-13 [1] CRAN (R 4.4.1)
plyr 1.8.9 2023-10-02 [1] CRAN (R 4.4.1)
png 0.1-8 2022-11-29 [1] CRAN (R 4.4.1)
polyclip 1.10-7 2024-07-23 [1] CRAN (R 4.4.1)
progressr 0.15.1 2024-11-22 [1] CRAN (R 4.4.2)
promises 1.3.2 2024-11-28 [1] CRAN (R 4.4.2)
purrr * 1.0.4 2025-02-05 [1] CRAN (R 4.4.2)
R.methodsS3 1.8.2 2022-06-13 [1] RSPM
R.oo 1.27.0 2024-11-01 [1] RSPM
R.utils 2.13.0 2025-02-24 [1] RSPM
R6 2.6.1 2025-02-15 [1] CRAN (R 4.4.2)
RANN 2.6.2 2024-08-25 [1] CRAN (R 4.4.1)
RColorBrewer * 1.1-3 2022-04-03 [1] CRAN (R 4.4.1)
Rcpp 1.0.14 2025-01-12 [1] CRAN (R 4.4.2)
RcppAnnoy 0.0.22 2024-01-23 [1] CRAN (R 4.4.1)
RcppHNSW 0.6.0 2024-02-04 [1] CRAN (R 4.4.1)
RcppParallel 5.1.9 2024-08-19 [1] CRAN (R 4.4.1)
readr * 2.1.5 2024-01-10 [1] CRAN (R 4.4.1)
rematch2 2.1.2 2020-05-01 [1] CRAN (R 4.4.1)
remotes 2.5.0 2024-03-17 [1] CRAN (R 4.4.1)
repr 1.1.7 2024-03-22 [1] CRAN (R 4.4.1)
reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.4.1)
reticulate 1.40.0 2024-11-15 [1] CRAN (R 4.4.1)
rhdf5 2.46.1 2023-11-29 [1] Bioconductor 3.18 (R 4.4.2)
rhdf5filters 1.14.1 2023-11-06 [1] Bioconductor
Rhdf5lib 1.24.2 2024-02-07 [1] Bioconductor 3.18 (R 4.4.2)
rlang 1.1.5 2025-01-17 [1] CRAN (R 4.4.2)
rmarkdown 2.29 2024-11-04 [1] CRAN (R 4.4.1)
ROCR 1.0-11 2020-05-02 [1] CRAN (R 4.4.1)
rprojroot 2.0.4 2023-11-05 [1] CRAN (R 4.4.1)
RSpectra 0.16-2 2024-07-18 [1] CRAN (R 4.4.1)
rstantools 2.4.0 2024-01-31 [1] CRAN (R 4.4.1)
rsvd 1.0.5 2021-04-16 [1] RSPM
Rtsne 0.17 2023-12-07 [1] CRAN (R 4.4.1)
scales 1.3.0 2023-11-28 [1] CRAN (R 4.4.1)
scattermore 1.2 2023-06-12 [1] CRAN (R 4.4.1)
scCustomize * 3.0.1 2025-03-07 [1] Github (samuel-marsh/scCustomize@3299b95)
schard 0.0.1 2025-05-06 [1] Github (cellgeni/schard@1b62a7c)
sctransform 0.4.1 2023-10-19 [1] CRAN (R 4.4.1)
sessioninfo 1.2.3 2025-02-05 [1] CRAN (R 4.4.2)
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SeuratObject * 5.0.2 2024-05-08 [1] CRAN (R 4.4.3)
SeuratWrappers * 0.4.0 2025-03-07 [1] Github (satijalab/seurat-wrappers@a1eb0d8)
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shiny 1.10.0 2024-12-14 [1] CRAN (R 4.4.2)
skimr * 2.1.5 2022-12-23 [1] CRAN (R 4.4.1)
snakecase 0.11.1 2023-08-27 [1] RSPM
sp * 2.2-0 2025-02-01 [1] CRAN (R 4.4.2)
spam 2.11-1 2025-01-20 [1] CRAN (R 4.4.2)
spatstat.data 3.1-4 2024-11-15 [1] CRAN (R 4.4.2)
spatstat.explore 3.3-4 2025-01-08 [1] CRAN (R 4.4.2)
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spatstat.sparse 3.1-0 2024-06-21 [1] CRAN (R 4.4.1)
spatstat.univar 3.1-1 2024-11-05 [1] CRAN (R 4.4.2)
spatstat.utils 3.1-2 2025-01-08 [1] CRAN (R 4.4.2)
statsExpressions 1.6.2 2024-12-02 [1] CRAN (R 4.4.2)
stringi 1.8.4 2024-05-06 [1] CRAN (R 4.4.1)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.4.1)
survival 3.8-3 2024-12-17 [1] CRAN (R 4.4.2)
tensor 1.5 2012-05-05 [1] CRAN (R 4.4.1)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.4.1)
tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.4.1)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.1)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.4.1)
timechange 0.3.0 2024-01-18 [1] CRAN (R 4.4.1)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.4.1)
UpSetR * 1.4.0 2019-05-22 [1] CRAN (R 4.4.2)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.1)
uwot 0.2.3 2025-02-24 [1] CRAN (R 4.4.2)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.1)
vipor 0.4.7 2023-12-18 [1] RSPM
viridis * 0.6.5 2024-01-29 [1] RSPM
viridisLite * 0.4.2 2023-05-02 [1] CRAN (R 4.4.1)
vroom 1.6.5 2023-12-05 [1] CRAN (R 4.4.1)
withr 3.0.2 2024-10-28 [1] CRAN (R 4.4.1)
xfun 0.51 2025-02-19 [1] CRAN (R 4.4.2)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.1)
yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.1)
zeallot 0.1.0 2018-01-28 [1] CRAN (R 4.4.1)
zoo 1.8-13 2025-02-22 [1] CRAN (R 4.4.2)
[1] /data/1_heteroAstrocytes/.pixi/envs/default/lib/R/library
* ── Packages attached to the search path.
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