Exploratory analysis of Cnr1 in mouse Onecut3-GABAergic and dopaminergic hypothalamic neurons during development

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Last updated: 2026-02-04

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Knit directory: PeVN-dopaminergic-Cnr1/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/eda.Rmd) and HTML (docs/eda.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	eb5ae7b	Evgenii O. Tretiakov	2026-02-04	fix(viz): update eda.Rmd to have clearly empty umap scatter plot
html	cd95a83	Evgenii O. Tretiakov	2026-02-04	feat(gene): fix rendering library problem
Rmd	d07340d	Evgenii O. Tretiakov	2026-02-04	feat(gene): add missed Cnr2
html	4cd8537	Evgenii O. Tretiakov	2023-05-29	Build site.
Rmd	62b0185	Evgenii O. Tretiakov	2023-05-29	specify analysis to cnr1
html	62b0185	Evgenii O. Tretiakov	2023-05-29	specify analysis to cnr1
Rmd	aaece06	Evgenii O. Tretiakov	2023-05-29	from Romanov et al 2020 subset GABAergic and dopaminergic Onecut3 population and use Arc-TIDA for comparison
html	aaece06	Evgenii O. Tretiakov	2023-05-29	from Romanov et al 2020 subset GABAergic and dopaminergic Onecut3 population and use Arc-TIDA for comparison

---

# Load tidyverse infrastructure packages
library(here)
library(tidyverse)
library(magrittr)
library(zeallot)
library(future)

# Load packages for scRNA-seq analysis and visualisation
library(Seurat)
library(SeuratDisk)
library(UpSetR)
library(patchwork)
library(RColorBrewer)

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

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

# Keep execution sequential to avoid large memory spikes during rendering.
plan("sequential")
options(
  future.globals.maxSize = Inf,
  future.rng.onMisuse = "ignore"
)
plan()

sequential:
- args: function (..., envir = parent.frame(), workers = "<NULL>")
- tweaked: FALSE
- call: plan(sequential)
FutureBackend to be launched

Read data

rar2020_ages_all <- c("E15", "E17", "P00", "P02", "P10", "P23")
rar2020_ages_postnat <- c("P02", "P10", "P23")
samples_df <- read_tsv(here("data/samples.tsv"))
colours_wtree <- setNames(
  read_lines(here(data_dir, "colours_wtree.tsv")),
  1:45
)

rar2020_srt_pub <-
  readr::read_rds(file.path(data_dir, "oldCCA_nae_srt.rds"))
rar2020_srt_pub %<>% UpdateSeuratObject()
colnames(rar2020_srt_pub@reductions$umap@cell.embeddings) <-
  c("UMAP_1", "UMAP_2")

rar2020_srt_pub$orig.ident <-
  rar2020_srt_pub %>%
  colnames() %>%
  str_split(pattern = ":", simplify = TRUE) %>%
  .[, 1] %>%
  plyr::mapvalues(
    x = .,
    from = samples_df$fullname,
    to = samples_df$sample
  )
rar2020_srt_pub$age <-
  plyr::mapvalues(
    x = rar2020_srt_pub$orig.ident,
    from = samples_df$sample,
    to = samples_df$age
  )
Idents(rar2020_srt_pub) <- "wtree"
neurons <-
  subset(rar2020_srt_pub, idents = c("18", "32"))
neurons <- RenameIdents(neurons, "18" = "PeVN_Onecut3", "32" = "Arc_TIDA")
neurons$celltype <- Idents(neurons)
neurons <-
  subset(neurons, subset = Slc17a6 == 0)
onecut3 <-
  subset(neurons,
    subset = Onecut3 > 0 | Th > 0 | Ddc > 0 | Slc6a3 > 0
  )

onecut3 <-
  Store_Palette_Seurat(
    seurat_object = onecut3,
    palette = rev(brewer.pal(n = 11, name = "Spectral")),
    palette_name = "div_Colour_Pal"
  )

Thus we subset the dataset to the neurons of interest from cluster 18, which are GABAergic and dopaminergic neurons expressing Onecut3 transcription factor. We also explicitly exclude Slc17a6-expressing neurons, which are glutamatergic neurons just in case to reduce noise. As the control group we use dopaminergic TIDA-neurons from the arcuate nucleus.

Derive and filter matrix of neurons of interest

oc3_genes <- unique(c(
  "Onecut3", "Cnr1", "Cnr2", "Slc32a1", "Gpr55", "Th",
  "Gad1", "Gad2", "Onecut2", "Prlr", "Ddc", "Slc6a3"
))

mtx_oc3 <-
  onecut3 %>%
  GetAssayData(assay = "RNA", layer = "data") %>%
  .[oc3_genes, , drop = FALSE] %>%
  as.matrix() %>%
  t()
rownames(mtx_oc3) <- colnames(onecut3)

min_filt_vector <- apply(mtx_oc3, 2, quantile, probs = 0.1)

# Prepare table of intersection sets analysis
content_mtx_oc3 <-
  (mtx_oc3 > min_filt_vector) %>%
  as_tibble() %>%
  mutate_all(as.numeric)

mtx_oc3_df <- as.data.frame(mtx_oc3)

Plot UMAPs density

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Onecut3"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-onecut3-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Th"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Th-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Slc6a3"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Slc6a3-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Prlr"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Prlr-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Cnr1"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Cnr1-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

scCustomize::FeaturePlot_scCustom(
  seurat_object = onecut3,
  features = "Cnr2",
  colors_use = onecut3@misc$div_Colour_Pal)

Past versions of plt-init-umap-density-Cnr2-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04

Cnr2 expression sanity check

cnr2_expr <- NULL
rna_data <- Seurat::GetAssayData(onecut3, assay = "RNA", layer = "data")
if ("Cnr2" %in% rownames(rna_data)) {
  cnr2_expr <- rna_data["Cnr2", ]
} else {
  cnr2_expr <- rep(0, ncol(onecut3))
  message("Cnr2 not present in RNA assay; treating as zero.")
}

cnr2_df <- tibble::tibble(expr = as.numeric(cnr2_expr))
cnr2_summary <- cnr2_df %>%
  summarise(
    n = n(),
    n_expressed = sum(expr > 0),
    pct_expressed = 100 * mean(expr > 0),
    max_expr = max(expr)
  )
cnr2_summary

ggplot(cnr2_df, aes(x = expr)) +
  geom_histogram(binwidth = 0.05, fill = "#a840ff", color = "white") +
  geom_vline(xintercept = 0, linetype = "dashed", color = "black") +
  labs(
    title = "Cnr2 expression in Onecut3 cells",
    subtitle = sprintf(
      "Cells > 0: %s (%.2f%%)",
      cnr2_summary$n_expressed,
      cnr2_summary$pct_expressed
    ),
    x = "log-normalized expression",
    y = "Cells"
  ) +
  theme_minimal(base_size = 12)

Past versions of plot-cnr2-expression-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Gpr55"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Gpr55-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Mgll"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Mgll-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Dagla"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Dagla-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Daglb"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Daglb-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Faah"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Faah-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Napepld"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Napepld-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Gde1"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Gde1-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Pparg"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Pparg-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Slc32a1"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-slc32a1-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Gad1"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-gad1-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Custom(
  seurat_object = onecut3,
  features = c("Gad2"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-gad2-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Joint density UMAP’s plots

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Onecut3", "Cnr1"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Onecut3-Cnr1-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Onecut3", "Cnr2"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Onecut3-Cnr2-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Onecut3", "Th"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Onecut3-Th-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Cnr1", "Th"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Cnr1-Th-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Cnr2", "Th"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Cnr2-Th-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Prlr", "Cnr1"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Prlr-Cnr1-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Prlr", "Cnr2"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Prlr-Cnr2-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Prlr", "Th"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Prlr-Th-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Prlr", "Onecut3"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Prlr-Onecut3-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Gpr55", "Prlr"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Gpr55-Prlr-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Gpr55", "Cnr1"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Gpr55-Cnr1-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Gpr55", "Cnr2"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Gpr55-Cnr2-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Gpr55", "Th"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Gpr55-Th-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Plot_Density_Joint_Only(
  seurat_object = onecut3,
  features = c("Gpr55", "Onecut3"),
  custom_palette = onecut3@misc$div_Colour_Pal
)

Past versions of plt-init-umap-density-Gpr55-Onecut3-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Correlation analysis visualisation between different genes

p_corrs <- list(
  plot_scatter_stats(
    mtx_oc3_df,
    x = Onecut3,
    y = Cnr1,
    xfill = "#ffc400",
    yfill = "#e22ee2"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    x = Onecut3,
    y = Cnr2,
    xfill = "#ffc400",
    yfill = "#a840ff"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    x = Slc32a1,
    y = Onecut3,
    xfill = "#0000da",
    yfill = "#ffc400"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    x = Gpr55,
    y = Onecut3,
    xfill = "#006eff",
    yfill = "#ffc400"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    x = Slc32a1,
    y = Cnr1,
    xfill = "#0000da",
    yfill = "#e22ee2"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    x = Slc32a1,
    y = Cnr2,
    xfill = "#0000da",
    yfill = "#a840ff"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    x = Gpr55,
    y = Cnr1,
    xfill = "#006eff",
    yfill = "#e22ee2"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    x = Gpr55,
    y = Cnr2,
    xfill = "#006eff",
    yfill = "#a840ff"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    x = Slc32a1,
    y = Gpr55,
    xfill = "#0000da",
    yfill = "#006eff"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    y = Slc32a1,
    x = Onecut3,
    yfill = "#0000da",
    xfill = "#ffc400"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    y = Gpr55,
    x = Onecut3,
    yfill = "#006eff",
    xfill = "#ffc400"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    y = Th,
    x = Onecut3,
    yfill = "#ff0000",
    xfill = "#ffc400"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    y = Gad1,
    x = Onecut3,
    yfill = "#a50202",
    xfill = "#ffc400"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    y = Gad2,
    x = Onecut3,
    yfill = "#4002a5",
    xfill = "#ffc400"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    y = Onecut2,
    x = Onecut3,
    yfill = "#6402a5",
    xfill = "#ffc400"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    y = Prlr,
    x = Onecut3,
    yfill = "#2502a5",
    xfill = "#ffc400"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    y = Ddc,
    x = Onecut3,
    yfill = "#4002a5",
    xfill = "#ffc400"
  ),
  plot_scatter_stats(
    mtx_oc3_df,
    y = Slc6a3,
    x = Onecut3,
    yfill = "#2502a5",
    xfill = "#ffc400"
  )
)
n_corrs <- list(
  "oc3-rna-data-Onecut3-Cnr1",
  "oc3-rna-data-Onecut3-Cnr2",
  "oc3-rna-data-Slc32a1-Onecut3",
  "oc3-rna-data-Gpr55-Onecut3",
  "oc3-rna-data-Slc32a1-Cnr1",
  "oc3-rna-data-Slc32a1-Cnr2",
  "oc3-rna-data-Gpr55-Cnr1",
  "oc3-rna-data-Gpr55-Cnr2",
  "oc3-rna-data-Slc32a1-Gpr55",
  "oc3-rna-data-Onecut3-Slc32a1",
  "oc3-rna-data-Onecut3-Gpr55",
  "oc3-rna-data-Onecut3-Th",
  "oc3-rna-data-Onecut3-Gad1",
  "oc3-rna-data-Onecut3-Gad2",
  "oc3-rna-data-Onecut3-Onecut2",
  "oc3-rna-data-Onecut3-Prlr",
  "oc3-rna-data-Onecut3-Ddc",
  "oc3-rna-data-Onecut3-Slc6a3"
)

walk2(n_corrs, p_corrs, save_my_plot, type = "stat-corr-plt")

Visualise intersections sets that we are going to use (highlighted)

upset(
  as.data.frame(content_mtx_oc3),
  order.by = "freq",
  sets.x.label = "Number of cells",
  text.scale = c(2, 1.6, 2, 1.3, 2, 3),
  nsets = 15,
  sets = c("Th", "Onecut3"),
  queries = list(
    list(
      query = intersects,
      params = list("Onecut3"),
      active = T
    ),
    list(
      query = intersects,
      params = list("Th"),
      active = T
    ),
    list(
      query = intersects,
      params = list("Th", "Onecut3"),
      active = T
    )
  ),
  nintersects = 60,
  empty.intersections = "on"
)

Past versions of plot-upset-oc3-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

upset(
  as.data.frame(content_mtx_oc3),
  order.by = "freq",
  sets.x.label = "Number of cells",
  text.scale = c(2, 1.6, 2, 1.3, 2, 3),
  nsets = 15,
  sets = c("Gpr55", "Cnr1", "Cnr2", "Slc32a1", "Th"),
  queries = list(
    list(
      query = intersects,
      params = list("Gpr55", "Slc32a1"),
      active = T
    ),
    list(
      query = intersects,
      params = list("Cnr1", "Th"),
      active = T
    )
  ),
  nintersects = 60,
  empty.intersections = "on"
)

Past versions of plot-upset-oc3-mod-1.png

Version	Author	Date
d07340d	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Regroup factor by stages for more balanced groups

onecut3$age %>% forcats::fct_count()

onecut3$stage <-
  onecut3$age %>%
  forcats::fct_collapse(
    `Embrionic day 15` = "E15",
    `Embrionic day 17` = "E17",
    Neonatal = c("P00", "P02"),
    Postnatal = c("P10", "P23")
  )
onecut3$stage %>% forcats::fct_count()

Make subset of stable neurons

onecut3$gaba_status <-
  content_mtx_oc3 %>%
  select(Gad1, Gad2, Slc32a1) %>%
  mutate(gaba = if_all(.fns = ~ .x > 0)) %>%
  .$gaba

onecut3$gaba_occurs <-
  content_mtx_oc3 %>%
  select(Gad1, Gad2, Slc32a1) %>%
  mutate(gaba = if_any(.fns = ~ .x > 0)) %>%
  .$gaba

onecut3$th_status <-
  content_mtx_oc3 %>%
  select(Th, Ddc, Slc6a3) %>%
  mutate(dopamin = if_any(.fns = ~ .x > 0)) %>%
  .$dopamin

oc3_fin <- onecut3

Check contingency tables for neurotransmitter signature

oc3_fin@meta.data %>%
  janitor::tabyl(th_status, gaba_status)

By age

oc3_fin@meta.data %>%
  janitor::tabyl(age, th_status)

By stage

oc3_fin@meta.data %>%
  janitor::tabyl(stage, th_status)

Make splits of neurons by neurotransmitter signature

oc3_fin$status <- oc3_fin$th_status %>%
  if_else(true = "dopaminergic",
    false = "GABAergic"
  )
Idents(oc3_fin) <- "status"
tryCatch(
  SaveH5Seurat(
    object    = oc3_fin,
    filename  = here(data_dir, "oc3_fin"),
    overwrite = TRUE,
    verbose   = TRUE
  ),
  error = function(e) {
    message("SaveH5Seurat skipped: ", e$message)
  }
)

## Split on basis of neurotrans and test for difference
oc3_fin_neurotrans <- SplitObject(oc3_fin, split.by = "status")

## Split on basis of age and test for difference
oc3_fin_ages <- SplitObject(oc3_fin, split.by = "age")

DotPlots grouped by age

Expression of GABA receptors in GABAergic Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$GABAergic,
  features = gabar,
  group.by = "age",
  cols = c("#adffff", "#0084ff"),
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-age-gabar-gaba-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Expression of GABA receptors in dopaminergic TIDA or Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$dopaminergic,
  features = gabar,
  group.by = "age",
  split.by = "celltype",
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-age-gabar-dop-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
4cd8537	Evgenii O. Tretiakov	2023-05-29
62b0185	Evgenii O. Tretiakov	2023-05-29
aaece06	Evgenii O. Tretiakov	2023-05-29

Expression of glutamate receptors in GABAergic Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$GABAergic,
  features = glutr,
  group.by = "age",
  cols = c("#adffff", "#0084ff"),
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-age-glur-gaba-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Expression of glutamate receptors in dopaminergic TIDA or Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$dopaminergic,
  features = glutr,
  group.by = "age",
  split.by = "celltype",
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-age-glur-dop-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
4cd8537	Evgenii O. Tretiakov	2023-05-29
62b0185	Evgenii O. Tretiakov	2023-05-29
aaece06	Evgenii O. Tretiakov	2023-05-29

DotPlots grouped by stage

Expression of GABA receptors in GABAergic Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$GABAergic,
  features = gabar,
  group.by = "stage",
  cols = c("#adffff", "#0084ff"),
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-stage-gabar-gaba-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Expression of GABA receptors in dopaminergic TIDA or Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$dopaminergic,
  features = gabar,
  group.by = "stage",
  split.by = "celltype",
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-stage-gabar-dop-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
4cd8537	Evgenii O. Tretiakov	2023-05-29
62b0185	Evgenii O. Tretiakov	2023-05-29
aaece06	Evgenii O. Tretiakov	2023-05-29

Expression of glutamate receptors in GABAergic Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$GABAergic,
  features = glutr,
  group.by = "stage",
  cols = c("#adffff", "#0084ff"),
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-stage-glur-gaba-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Expression of glutamate receptors in dopaminergic TIDA or Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$dopaminergic,
  features = glutr,
  group.by = "stage",
  split.by = "celltype",
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-stage-glur-dop-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
4cd8537	Evgenii O. Tretiakov	2023-05-29
62b0185	Evgenii O. Tretiakov	2023-05-29
aaece06	Evgenii O. Tretiakov	2023-05-29

Expression of neuromodulators receptors in GABAergic Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$GABAergic,
  features = npr,
  group.by = "stage",
  cols = c("#adffff", "#0084ff"),
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-stage-npr-gaba-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
62b0185	Evgenii O. Tretiakov	2023-05-29
aaece06	Evgenii O. Tretiakov	2023-05-29

Expression of neuromodulators receptors in dopaminergic TIDA or Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$dopaminergic,
  features = npr,
  group.by = "stage",
  split.by = "celltype",
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-stage-npr-dop-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
4cd8537	Evgenii O. Tretiakov	2023-05-29
62b0185	Evgenii O. Tretiakov	2023-05-29
aaece06	Evgenii O. Tretiakov	2023-05-29

Expression of endocannabinoids relevant genes in GABAergic Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$GABAergic,
  features = cnbn,
  group.by = "stage",
  cols = c("#adffff", "#0084ff"),
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-stage-cnbn-gaba-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
aaece06	Evgenii O. Tretiakov	2023-05-29

Expression of endocannabinoids relevant genes in dopaminergic TIDA or Onecut3 positive cells

DotPlot(
  object = oc3_fin_neurotrans$dopaminergic,
  features = cnbn,
  group.by = "stage",
  split.by = "celltype",
  col.min = -1, col.max = 1
) + RotatedAxis()

Past versions of plot-dotplot-stage-cnbn-dop-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
4cd8537	Evgenii O. Tretiakov	2023-05-29
62b0185	Evgenii O. Tretiakov	2023-05-29
aaece06	Evgenii O. Tretiakov	2023-05-29

Overrepresentation analysis

Idents(oc3_fin) <- "status"

sbs_mtx_oc <-
  oc3_fin %>%
  GetAssayData(assay = "RNA", layer = "data") %>%
  .[unique(c(neurotrans, cnbn, "Prlr", "Onecut3", "Th")), , drop = FALSE] %>%
  as.matrix() %>%
  t()
rownames(sbs_mtx_oc) <- colnames(oc3_fin)

min_filt_vector2 <- apply(sbs_mtx_oc, 2, quantile, probs = 0.005)

# Prepare table of intersection sets analysis
content_sbs_mtx_oc <-
  (sbs_mtx_oc > min_filt_vector2) %>%
  as_tibble() %>%
  mutate_all(as.numeric)

upset(
  as.data.frame(content_sbs_mtx_oc),
  order.by = "freq",
  sets.x.label = "Number of cells",
  text.scale = c(2, 1.6, 2, 1.3, 2, 3),
  nsets = 15,
  sets = c(
    "Th", "Onecut3",
    cnbn, "Prlr"
  ) %>%
    .[. %in% colnames(content_sbs_mtx_oc)],
  nintersects = 20,
  empty.intersections = NULL
)

Past versions of plot-upset-sbs-oc3-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
62b0185	Evgenii O. Tretiakov	2023-05-29
aaece06	Evgenii O. Tretiakov	2023-05-29

upset(
  as.data.frame(content_sbs_mtx_oc),
  order.by = "freq",
  sets.x.label = "Number of cells",
  text.scale = c(2, 1.6, 2, 1.3, 2, 3),
  nsets = 15,
  sets = c("Cnr1", "Cnr2", "Prlr", "Th", "Onecut3") %>%
    .[. %in% colnames(content_sbs_mtx_oc)],
  nintersects = 10,
  empty.intersections = NULL
)

Past versions of plot-upset-sbs-oc3-mod-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
62b0185	Evgenii O. Tretiakov	2023-05-29
aaece06	Evgenii O. Tretiakov	2023-05-29

sbs_mtx_oc_full <- content_sbs_mtx_oc |>
  select(any_of(c(
    neurotrans, cnbn, "Prlr", "Cnr1", "Gpr55", "Onecut3"
  ))) |>
  dplyr::bind_cols(oc3_fin@meta.data)

sbs_mtx_oc_full |> glimpse()

Rows: 401
Columns: 48
$ Slc17a6          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Slc17a7          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Slc17a8          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Slc1a1           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
$ Slc1a2           <dbl> 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,…
$ Slc1a6           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Gad1             <dbl> 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,…
$ Slc32a1          <dbl> 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,…
$ Slc6a1           <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0,…
$ Cnr1             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
$ Cnr2             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Gpr55            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Dagla            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
$ Daglb            <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Mgll             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Faah             <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1,…
$ Napepld          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Trpv1            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Gde1             <dbl> 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1,…
$ Pparg            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Prlr             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Onecut3          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ nGene            <int> 3034, 2551, 2029, 1415, 1513, 1079, 3160, 3072, 2778,…
$ nUMI             <dbl> 6868, 5316, 3696, 2667, 2490, 1789, 8247, 8544, 6478,…
$ orig.ident       <chr> "FC2P23", "FC2P23", "FC2P23", "FC2P23", "FC2P23", "FC…
$ res.0.2          <chr> "5", "5", "5", "5", "5", "5", "5", "5", "5", "5", "5"…
$ res.0.4          <chr> "26", "26", "26", "26", "26", "26", "26", "26", "26",…
$ res.0.8          <chr> "31", "31", "31", "31", "31", "31", "31", "31", "31",…
$ res.1.2          <chr> "36", "36", "36", "36", "36", "36", "36", "36", "36",…
$ res.2            <chr> "37", "37", "37", "37", "37", "37", "37", "37", "37",…
$ tree.ident       <int> 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 2…
$ pro_Inter        <chr> "17", "17", "17", "17", "17", "17", "17", "17", "17",…
$ pro_Enter        <chr> "17", "17", "17", "17", "17", "17", "17", "17", "17",…
$ tree_final       <fct> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1…
$ subtree          <fct> 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 1…
$ prim_walktrap    <fct> 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 2…
$ umi_per_gene     <dbl> 2.263678, 2.083889, 1.821587, 1.884806, 1.645737, 1.6…
$ log_umi_per_gene <dbl> 0.3548147, 0.3188745, 0.2604499, 0.2752666, 0.2163604…
$ nCount_RNA       <dbl> 6868, 5316, 3696, 2667, 2490, 1789, 8247, 8544, 6478,…
$ nFeature_RNA     <int> 3034, 2551, 2029, 1415, 1513, 1079, 3160, 3072, 2778,…
$ wtree            <fct> 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 3…
$ age              <chr> "P23", "P23", "P23", "P23", "P23", "P23", "P10", "P10…
$ celltype         <fct> Arc_TIDA, Arc_TIDA, Arc_TIDA, Arc_TIDA, Arc_TIDA, Arc…
$ stage            <fct> Postnatal, Postnatal, Postnatal, Postnatal, Postnatal…
$ gaba_status      <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE…
$ gaba_occurs      <lgl> TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRU…
$ th_status        <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
$ status           <chr> "dopaminergic", "dopaminergic", "dopaminergic", "dopa…

sbs_mtx_oc_full$CB1R <-
  sbs_mtx_oc_full %>%
  select(Cnr1, Gpr55) %>%
  mutate(CB1R = Cnr1 > 0) %>%
  .$CB1R

sbs_mtx_oc_full$PRLR <-
  sbs_mtx_oc_full %>%
  select(Prlr) %>%
  mutate(PRLR = Prlr > 0) %>%
  .$PRLR

sbs_mtx_oc_full$oc3 <-
  (sbs_mtx_oc_full$Onecut3 > 0)

# for reproducibility
set.seed(123)

# plot
plot_grouped_pie(
  data = sbs_mtx_oc_full,
  x = CB1R,
  y = oc3,
  grouping.var = status,
  palette = "category10_d3",
  title.text = "Cnr1 specification of onecut-driven hypothalamic neuronal lineages by Onecut3 and main neurotransmitter expression",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant"
)

Past versions of plot-piecharts-sbs-oc3-chisqr-1.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
62b0185	Evgenii O. Tretiakov	2023-05-29
aaece06	Evgenii O. Tretiakov	2023-05-29

plot_grouped_pie(
  data = sbs_mtx_oc_full,
  x = PRLR,
  y = CB1R,
  grouping.var = status,
  palette = "category10_d3",
  title.text = "Prlr specification of onecut-driven PeVN or TIDA hypothalamic neuronal lineages by Cnr1 and main neurotransmitter expression",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant"
)

Past versions of plot-piecharts-sbs-oc3-chisqr-2.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
62b0185	Evgenii O. Tretiakov	2023-05-29

plot_grouped_pie(
  data = sbs_mtx_oc_full,
  x = PRLR,
  y = CB1R,
  grouping.var = celltype,
  palette = "category10_d3",
  title.text = "Prlr specification of onecut-driven PeVN or TIDA hypothalamic neuronal lineages by Cnr1",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant"
)

Past versions of plot-piecharts-sbs-oc3-chisqr-3.png

Version	Author	Date
cd95a83	Evgenii O. Tretiakov	2026-02-04
62b0185	Evgenii O. Tretiakov	2023-05-29

Session information

sessionInfo()

R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin24.6.0
Running under: macOS Tahoe 26.3

Matrix products: default
BLAS:   /nix/store/6mzxjk9lajhz47524h5p165prxjdmivx-blas-3/lib/libblas.dylib 
LAPACK: /nix/store/6r1gd6cr5bcp8rj0rs750gdygpci74l1-openblas-0.3.30/lib/libopenblasp-r0.3.30.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Vienna
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] RColorBrewer_1.1-3    patchwork_1.3.2       UpSetR_1.4.0         
 [4] SeuratDisk_0.0.0.9021 Seurat_5.4.0          SeuratObject_5.3.0   
 [7] sp_2.2-0              future_1.69.0         zeallot_0.2.0        
[10] magrittr_2.0.4        lubridate_1.9.4       forcats_1.0.0        
[13] stringr_1.6.0         dplyr_1.2.0           purrr_1.2.1          
[16] readr_2.1.5           tidyr_1.3.2           tibble_3.3.1         
[19] ggplot2_4.0.2         tidyverse_2.0.0       here_1.0.2           
[22] workflowr_1.7.2      

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.23       splines_4.5.1          later_1.4.5           
  [4] polyclip_1.10-7        janitor_2.2.1          fastDummies_1.7.5     
  [7] lifecycle_1.0.5        rprojroot_2.1.1        globals_0.19.0        
 [10] processx_3.8.6         lattice_0.22-7         vroom_1.6.5           
 [13] hdf5r_1.3.12           MASS_7.3-65            plotly_4.12.0         
 [16] sass_0.4.10            rmarkdown_2.30         jquerylib_0.1.4       
 [19] yaml_2.3.12            httpuv_1.6.16          otel_0.2.0            
 [22] sctransform_0.4.2      spam_2.11-1            spatstat.sparse_3.1-0 
 [25] reticulate_1.44.1      cowplot_1.2.0          pbapply_1.7-4         
 [28] abind_1.4-8            Rtsne_0.17             git2r_0.36.2          
 [31] circlize_0.4.17        ggrepel_0.9.6          irlba_2.3.5.1         
 [34] listenv_0.10.0         spatstat.utils_3.2-1   goftest_1.2-3         
 [37] RSpectra_0.16-2        spatstat.random_3.4-4  fitdistrplus_1.2-6    
 [40] parallelly_1.46.1      codetools_0.2-20       scCustomize_3.0.1     
 [43] tidyselect_1.2.1       shape_1.4.6.1          farver_2.1.2          
 [46] matrixStats_1.5.0      spatstat.explore_3.7-0 jsonlite_2.0.0        
 [49] progressr_0.18.0       ggridges_0.5.7         survival_3.8-3        
 [52] systemfonts_1.2.3      tools_4.5.1            ragg_1.4.0            
 [55] ica_1.0-3              Rcpp_1.1.1             glue_1.8.0            
 [58] gridExtra_2.3          xfun_0.56              mgcv_1.9-3            
 [61] withr_3.0.2            fastmap_1.2.0          callr_3.7.6           
 [64] digest_0.6.39          timechange_0.3.0       R6_2.6.1              
 [67] mime_0.13              ggprism_1.0.7          textshaping_1.0.1     
 [70] colorspace_2.1-2       scattermore_1.2        tensor_1.5.1          
 [73] spatstat.data_3.1-9    generics_0.1.4         ggsci_4.2.0           
 [76] data.table_1.17.8      httr_1.4.7             htmlwidgets_1.6.4     
 [79] whisker_0.4.1          uwot_0.2.4             pkgconfig_2.0.3       
 [82] gtable_0.3.6           lmtest_0.9-40          S7_0.2.1              
 [85] htmltools_0.5.9        dotCall64_1.2          scales_1.4.0          
 [88] png_0.1-8              spatstat.univar_3.1-6  snakecase_0.11.1      
 [91] knitr_1.51             rstudioapi_0.17.1      tzdb_0.5.0            
 [94] reshape2_1.4.5         nlme_3.1-168           cachem_1.1.0          
 [97] zoo_1.8-15             GlobalOptions_0.1.3    KernSmooth_2.23-26    
[100] parallel_4.5.1         miniUI_0.1.2           vipor_0.4.7           
[103] ggrastr_1.0.2          pillar_1.11.1          grid_4.5.1            
[106] vctrs_0.7.1            RANN_2.6.2             promises_1.5.0        
[109] xtable_1.8-4           cluster_2.1.8.1        beeswarm_0.4.0        
[112] paletteer_1.6.0        evaluate_1.0.5         cli_3.6.5             
[115] compiler_4.5.1         rlang_1.1.7            crayon_1.5.3          
[118] future.apply_1.20.1    labeling_0.4.3         rematch2_2.1.2        
[121] ps_1.9.1               getPass_0.2-4          plyr_1.8.9            
[124] fs_1.6.6               ggbeeswarm_0.7.2       stringi_1.8.7         
[127] viridisLite_0.4.2      deldir_2.0-4           lazyeval_0.2.2        
[130] spatstat.geom_3.7-0    Matrix_1.7-3           RcppHNSW_0.6.0        
[133] hms_1.1.3              bit64_4.6.0-1          shiny_1.12.1          
[136] ROCR_1.0-12            igraph_2.1.4           bslib_0.10.0          
[139] bit_4.6.0