Plot results of Differential expression analysis for individual hypothalamic nuclei astrocytes evaluation dataset

Load data and setup parameters

Code

# 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(ggplot2)
  library(cowplot)
  library(patchwork)
  library(ggstatsplot)
  library(sceasy)
  library(Seurat)
  library(SeuratDisk)
  library(SeuratWrappers)
  library(schex)
  library(scCustomize)
})

Set paths

Code

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

Load helper functions and gene-sets

Code

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

Set fixed variables

Code

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

# Parameters for parallel execution
n_cores <- 8
plan("multisession", workers = n_cores)
options(
  future.globals.maxSize = 1999999 * 1024^2,
  future.rng.onMisuse = "ignore"
)
plan()

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

Code

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

Code

bioproject <- "individual_hypothalamic_nuclei_astrocytes_evaluation_dataset"
cb_fpr <- 0.001
connectivity_model <- "min_tree"
k <- 10
metric <- "euclidean"
signature <- 100

Load predicted astrocytes data

Code

srt_path <- here(
  data_dir,
  sprintf("best_xgboost-subregional-astrocytes_dataset-msp_%s-metric_%s-k_%s-sign_%s-amb_%s.h5Seurat", connectivity_model, metric, k, signature, cb_fpr)
)
srt <- LoadH5Seurat(file = srt_path)

srt <-
  Store_Palette_Seurat(
    seurat_object = srt,
    palette = c(
      "#ffff00", "#fae200", "#f4c500", "#eeab00", "#e99500",
      "#e37f00", "#dd6b00", "#d75b00", "#d04c00", "#c93e00",
      "#c33300", "#bc2800", "#b42003", "#ad1941", "#a41281",
      "#9c0db7", "#9309dd", "#8906f7", "#7f04ff", "#7402f6",
      "#6701db", "#5900b0", "#49007e", "#32003e", "#000000"
    ),
    palette_name = "expr_Colour_Palette_gnuplot_rette_gnuplot_r")

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

colours_code <- c(
  "0" = "grey30",
  "1" = "blue",
  "2" = "skyblue",
  "3" = "forestgreen",
  "4" = "greenyellow",
  "5" = "yellow",
  "6" = "orangered",
  "7" = "grey70"
)

colours_region <- c(
  "ARC" = "grey30",
  "LHA" = "blue",
  "MnPO" = "skyblue",
  "POA" = "forestgreen",
  "PVN" = "greenyellow",
  "SCN" = "yellow",
  "VMH" = "orangered",
  "VPH" = "grey70"
)

print(srt)

An object of class Seurat 
27381 features across 35642 samples within 1 assay 
Active assay: RNA (27381 features, 0 variable features)
 2 dimensional reductions calculated: densmap_logit, densmap_xgboost

Code

invisible(gc())

Code

srt <- NormalizeData(srt)
srt <- FindVariableFeatures(srt, selection.method = "vst", nfeatures = 5000)
all.genes <- rownames(srt)
srt <- ScaleData(srt, features = all.genes)

Code

selected_genes <- c(
  "Grpr", "Tbx3"
)

Code

Plot_Density_Custom(seurat_object = srt, reduction = "densmap_xgboost", features = c("Grpr", "Tbx3"))

Code

Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Grpr", "Tbx3"), custom_palette = srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Rax", "Tbx3"), custom_palette = srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Grpr", "Rax"), custom_palette = srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Rax", "Vim"), custom_palette = srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Rax", "Col25a1"), custom_palette = srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Rax", "Adm"), custom_palette = srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Tbx3", "Vim"), custom_palette = srt@misc$div_Colour_Pal)

Code

sbs_mtx <- GetAssayData(object = srt, slot = "count", assay = "RNA") %>%
  as.data.frame() %>%
  t() %>%
  as.data.frame() %>%
  select(Grpr, Tbx3, Rax, Vim, Nes, Adm, Col25a1) %>%
  dplyr::bind_cols(srt@meta.data) %>%
  select(region, bioproject, project, Grpr, Tbx3, Rax, Vim, Nes, Adm, Col25a1) %>%
  mutate(Grpr_pos = Grpr > 0,
         Tbx3_pos  = Tbx3  > 0,
         Rax_pos = Rax > 0,
         Nes_pos = Nes > 0,
         Vim_pos  = Vim  > 0)

sbs_mtx %>% skimr::skim()

Data summary

Name	Piped data
Number of rows	35642
Number of columns	15
_______________________
Column type frequency:
factor	3
logical	5
numeric	7
________________________
Group variables	None

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
region	0	1	FALSE	8	VMH: 24305, SCN: 5507, VPH: 1951, POA: 1706
bioproject	0	1	FALSE	11	PRJ: 20451, PRJ: 5119, PRJ: 3854, PRJ: 1951
project	0	1	FALSE	11	liu: 20451, wen: 5119, aff: 3854, mic: 1951

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
Grpr_pos	0	1	0.01	FAL: 35449, TRU: 193
Tbx3_pos	0	1	0.01	FAL: 35292, TRU: 350
Rax_pos	0	1	0.06	FAL: 33623, TRU: 2019
Nes_pos	0	1	0.01	FAL: 35436, TRU: 206
Vim_pos	0	1	0.04	FAL: 34132, TRU: 1510

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Grpr	0	1	0.01	0.12	0	0	0	0	6	▇▁▁▁▁
Tbx3	0	1	0.01	0.12	0	0	0	0	5	▇▁▁▁▁
Rax	0	1	0.07	0.36	0	0	0	0	16	▇▁▁▁▁
Vim	0	1	0.20	2.29	0	0	0	0	124	▇▁▁▁▁
Nes	0	1	0.01	0.08	0	0	0	0	3	▇▁▁▁▁
Adm	0	1	0.00	0.07	0	0	0	0	3	▇▁▁▁▁
Col25a1	0	1	0.31	3.39	0	0	0	0	128	▇▁▁▁▁

Code

# plot
grouped_ggpiestats(
  # arguments relevant for `ggpiestats()`
  data = sbs_mtx,
  x = Grpr_pos,
  y = Tbx3_pos,
  grouping.var = region,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "Grpr specification of Tbx3-positive hypothalamic astrocytic lineages",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
  plotgrid.args = list(nrow = 8)
)

Code

grouped_ggpiestats(
  # arguments relevant for `ggpiestats()`
  data = sbs_mtx,
  x = Grpr_pos,
  y = Tbx3_pos,
  grouping.var = project,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "Grpr specification of Tbx3-positive hypothalamic astrocytic lineages",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
  plotgrid.args = list(nrow = 8)
)

Code

# plot
grouped_ggpiestats(
  # arguments relevant for `ggpiestats()`
  data = sbs_mtx,
  x = Grpr_pos,
  y = Rax_pos,
  grouping.var = project,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "Grpr specification of Rax_positive hypothalamic astrocytic lineages",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
  plotgrid.args = list(nrow = 8)
)

Code

grouped_ggpiestats(
  # arguments relevant for `ggpiestats()`
  data = sbs_mtx,
  x = Tbx3_pos,
  y = Rax_pos,
  grouping.var = project,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "Tbx3 specification of Rax_positive hypothalamic astrocytic lineages",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
  plotgrid.args = list(nrow = 8)
)

Code

# plot
grouped_ggpiestats(
  # arguments relevant for `ggpiestats()`
  data = sbs_mtx,
  x = Grpr_pos,
  y = Vim_pos,
  grouping.var = project,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "Grpr specification of Vim_positive hypothalamic astrocytic lineages",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
  plotgrid.args = list(nrow = 8)
)

Code

grouped_ggpiestats(
  # arguments relevant for `ggpiestats()`
  data = sbs_mtx,
  x = Tbx3_pos,
  y = Vim_pos,
  grouping.var = project,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "Tbx3 specification of Vim_positive hypothalamic astrocytic lineages",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
  plotgrid.args = list(nrow = 8)
)

Code

# plot
grouped_ggpiestats(
  # arguments relevant for `ggpiestats()`
  data = sbs_mtx,
  x = Grpr_pos,
  y = Nes_pos,
  grouping.var = project,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "Grpr specification of Nes_positive hypothalamic astrocytic lineages",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
  plotgrid.args = list(nrow = 8)
)

Code

grouped_ggpiestats(
  # arguments relevant for `ggpiestats()`
  data = sbs_mtx,
  x = Tbx3_pos,
  y = Nes_pos,
  grouping.var = project,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "Tbx3 specification of Nes_positive hypothalamic astrocytic lineages",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
  plotgrid.args = list(nrow = 8)
)

Code

srt2 <- subset(srt, subset = Grpr > 1 | Tbx3 > 1)
FeaturePlot(srt2, c("Grpr", "Tbx3", "Rax", "Vim", "Nes", "Gfap"), reduction = "densmap_xgboost", ncol = 3, slot = "counts", order = TRUE)

Code

FeaturePlot_scCustom(srt2, c("Grpr", "Tbx3", "Rax", "Vim", "Nes", "Gfap"), reduction = "densmap_xgboost", num_columns = 3, slot = "data")

Code

FeaturePlot_scCustom(srt2, c("Grpr"), reduction = "densmap_xgboost", num_columns = 3, slot = "data", split.by = "project")

Code

FeaturePlot_scCustom(srt2, c("Tbx3"), reduction = "densmap_xgboost", num_columns = 3, slot = "data", split.by = "project")

Code

FeaturePlot_scCustom(srt2, c("Rax"), reduction = "densmap_xgboost", num_columns = 3, slot = "data", split.by = "project")

Code

FeaturePlot_scCustom(srt2, c("Vim"), reduction = "densmap_xgboost", num_columns = 3, slot = "data", split.by = "project")

Code

FeaturePlot_scCustom(srt2, c("Nes"), reduction = "densmap_xgboost", num_columns = 3, slot = "data", split.by = "project")

Code

FeaturePlot_scCustom(srt2, c("Grpr"), reduction = "densmap_xgboost", num_columns = 3, slot = "data", split.by = "region")

Code

FeaturePlot_scCustom(srt2, c("Tbx3"), reduction = "densmap_xgboost", num_columns = 3, slot = "data", split.by = "region")

Code

FeaturePlot_scCustom(srt2, c("Rax"), reduction = "densmap_xgboost", num_columns = 3, slot = "data", split.by = "region")

Code

FeaturePlot_scCustom(srt2, c("Vim"), reduction = "densmap_xgboost", num_columns = 3, slot = "data", split.by = "region")

Code

FeaturePlot_scCustom(srt2, c("Nes"), reduction = "densmap_xgboost", num_columns = 3, slot = "data", split.by = "region")

Code

FeatureScatter(srt2, "Grpr", "Tbx3")

Code

FeatureScatter(srt2, "Grpr", "Rax")

Code

FeatureScatter(srt2, "Rax", "Tbx3")

Code

FeatureScatter(srt2, "Grpr", "Vim")

Code

FeatureScatter(srt2, "Vim", "Tbx3")

Code

FeatureScatter(srt2, "Grpr", "Nes")

Code

FeatureScatter(srt2, "Nes", "Tbx3")

Code

FeatureScatter(srt2, "Vim", "Rax")

Code

FeatureScatter(srt2, "Rax", "Nes")

Code

FeatureScatter(srt2, "Vim", "Nes")

Code

mtx_export <- GetAssayData(object = srt, slot = "count", assay = "RNA") %>%
  as.data.frame() %>%
  t() %>%
  as.data.frame() %>%
  select(Grpr, Tbx3, Rax, Vim, Nes, Adm, Col25a1) %>%
  dplyr::bind_cols(srt@meta.data) %>%
  select(cell_name, region, bioproject, project,
         Grpr, Tbx3, Rax, Vim, Nes, Adm, Col25a1) %>%
  mutate(Grpr_pos = as.logical(Grpr),
         Tbx3_pos  = as.logical(Tbx3),
         Rax = as.logical(Rax),
         Vim = as.logical(Vim),
         Nes = as.logical(Nes),
         Adm = as.logical(Adm),
         Col25a1 = as.logical(Col25a1)) %>% 
  filter((Grpr_pos & (Rax | Vim | Nes | Adm | Col25a1 | Tbx3_pos)) |
           (Tbx3_pos & (Rax | Vim | Nes | Adm | Col25a1 | Grpr_pos)))
skim(mtx_export)

Data summary

Name	mtx_export
Number of rows	277
Number of columns	13
_______________________
Column type frequency:
character	1
factor	3
logical	7
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
cell_name	0	1	29	30	0	277	0

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
region	0	1	FALSE	7	VMH: 234, ARC: 24, VPH: 11, SCN: 5
bioproject	0	1	FALSE	9	PRJ: 181, PRJ: 53, PRJ: 24, PRJ: 11
project	0	1	FALSE	9	aff: 181, liu: 53, lut: 24, mic: 11

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
Rax	0	1	0.38	FAL: 172, TRU: 105
Vim	0	1	0.31	FAL: 192, TRU: 85
Nes	0	1	0.08	FAL: 256, TRU: 21
Adm	0	1	0.07	FAL: 258, TRU: 19
Col25a1	0	1	0.70	TRU: 194, FAL: 83
Grpr_pos	0	1	0.44	FAL: 155, TRU: 122
Tbx3_pos	0	1	0.65	TRU: 181, FAL: 96

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Grpr	0	1	0.65	0.95	0	0	0	1	6	▇▁▁▁▁
Tbx3	0	1	0.78	0.75	0	0	1	1	5	▇▁▁▁▁

Code

janitor::tabyl(mtx_export, region, Grpr_pos, Tbx3_pos)

$`FALSE`
 region FALSE TRUE
    ARC     0    9
    LHA     0    0
   MnPO     0    0
    POA     0    0
    PVN     0    0
    SCN     0    2
    VMH     0   84
    VPH     0    1

$`TRUE`
 region FALSE TRUE
    ARC    12    3
    LHA     1    0
   MnPO     0    0
    POA     1    0
    PVN     0    1
    SCN     3    0
    VMH   128   22
    VPH    10    0

Code

janitor::tabyl(mtx_export, region, Rax, Tbx3_pos)

$`FALSE`
 region FALSE TRUE
    ARC     8    1
    LHA     0    0
   MnPO     0    0
    POA     0    0
    PVN     0    0
    SCN     1    1
    VMH    43   41
    VPH     0    1

$`TRUE`
 region FALSE TRUE
    ARC    10    5
    LHA     1    0
   MnPO     0    0
    POA     1    0
    PVN     0    1
    SCN     3    0
    VMH   100   50
    VPH     5    5

Code

janitor::tabyl(mtx_export, region, Vim, Tbx3_pos)

$`FALSE`
 region FALSE TRUE
    ARC     2    7
    LHA     0    0
   MnPO     0    0
    POA     0    0
    PVN     0    0
    SCN     1    1
    VMH    57   27
    VPH     1    0

$`TRUE`
 region FALSE TRUE
    ARC    10    5
    LHA     0    1
   MnPO     0    0
    POA     0    1
    PVN     0    1
    SCN     0    3
    VMH   116   34
    VPH     5    5

Code

janitor::tabyl(mtx_export, region, Nes, Tbx3_pos)

$`FALSE`
 region FALSE TRUE
    ARC     8    1
    LHA     0    0
   MnPO     0    0
    POA     0    0
    PVN     0    0
    SCN     2    0
    VMH    78    6
    VPH     0    1

$`TRUE`
 region FALSE TRUE
    ARC    14    1
    LHA     1    0
   MnPO     0    0
    POA     1    0
    PVN     0    1
    SCN     3    0
    VMH   140   10
    VPH     9    1

Code

janitor::tabyl(mtx_export, region, Adm, Tbx3_pos)

$`FALSE`
 region FALSE TRUE
    ARC     9    0
    LHA     0    0
   MnPO     0    0
    POA     0    0
    PVN     0    0
    SCN     2    0
    VMH    83    1
    VPH     1    0

$`TRUE`
 region FALSE TRUE
    ARC    12    3
    LHA     1    0
   MnPO     0    0
    POA     1    0
    PVN     1    0
    SCN     3    0
    VMH   135   15
    VPH    10    0

Code

janitor::tabyl(mtx_export, region, Col25a1, Tbx3_pos)

$`FALSE`
 region FALSE TRUE
    ARC     4    5
    LHA     0    0
   MnPO     0    0
    POA     0    0
    PVN     0    0
    SCN     1    1
    VMH    34   50
    VPH     1    0

$`TRUE`
 region FALSE TRUE
    ARC     4   11
    LHA     1    0
   MnPO     0    0
    POA     1    0
    PVN     1    0
    SCN     1    2
    VMH    27  123
    VPH     8    2

Code

sbs_mtx_export <- 
  mtx_export |> 
  filter(Grpr_pos & Tbx3_pos) |> 
  select(cell_name, region, bioproject, project)

write_tsv(mtx_export,
          file = here(data_dir, "data/PRJNA722418", "output/tables",
                      "PRJNA722418-Grpr-Tbx3-astrocytes_dataset.tsv"))

srt3 <- subset(srt, cells = sbs_mtx_export$cell_name)

Code

Idents(object = srt, cells = mtx_export$cell_name) <- "Tbx3|Grpr"
Idents(object = srt, cells = sbs_mtx_export$cell_name) <- "Tbx3&Grpr"

Cluster_Highlight_Plot(
  seurat_object = srt,
  reduction = "densmap_xgboost",
  cluster_name = c("Tbx3&Grpr", "Tbx3|Grpr"),
  highlight_color = c("firebrick", "forestgreen"))

Session information

Code

sI <- sessioninfo::session_info()
sI$loadedOnly <- NULL
print(sI, locale = FALSE)

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.1 (2023-06-16)
 os       Ubuntu 22.04.3 LTS
 system   x86_64, linux-gnu
 ui       X11
 language en_US:en
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2023-09-26
 pandoc   3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version     date (UTC) lib source
 abind                  1.4-5       2016-07-21 [2] RSPM (R 4.3.0)
 base64enc              0.1-3       2015-07-28 [2] RSPM (R 4.3.0)
 BayesFactor            0.9.12-4.4  2022-07-05 [2] RSPM (R 4.3.0)
 bayestestR             0.13.1      2023-04-07 [2] RSPM (R 4.3.0)
 beeswarm               0.4.0       2021-06-01 [2] RSPM (R 4.3.0)
 Biobase              * 2.60.0      2023-04-25 [2] RSPM (R 4.3.1)
 BiocGenerics         * 0.46.0      2023-04-25 [2] RSPM (R 4.3.1)
 BiocManager            1.30.22     2023-08-08 [2] RSPM (R 4.3.0)
 bit                    4.0.5       2022-11-15 [2] RSPM (R 4.3.0)
 bit64                  4.0.5       2020-08-30 [2] RSPM (R 4.3.0)
 bitops                 1.0-7       2021-04-24 [2] RSPM (R 4.3.0)
 circlize               0.4.16      2023-08-17 [2] Github (jokergoo/circlize@11ddb74)
 cli                    3.6.1       2023-03-23 [2] RSPM (R 4.3.0)
 cluster                2.1.4       2022-08-22 [2] RSPM (R 4.3.0)
 coda                   0.19-4      2020-09-30 [2] RSPM (R 4.3.0)
 codetools              0.2-19      2023-02-01 [2] RSPM (R 4.3.0)
 colorspace             2.1-0       2023-01-23 [2] RSPM (R 4.3.0)
 concaveman             1.1.0       2020-05-11 [2] RSPM (R 4.3.0)
 correlation            0.8.4       2023-04-06 [2] RSPM (R 4.3.0)
 cowplot              * 1.1.1       2020-12-30 [2] RSPM (R 4.3.0)
 crayon                 1.5.2       2022-09-29 [2] RSPM (R 4.3.0)
 data.table             1.14.8      2023-02-17 [2] RSPM (R 4.3.0)
 datawizard             0.8.0       2023-06-16 [2] RSPM (R 4.3.0)
 DelayedArray           0.26.7      2023-07-28 [2] RSPM (R 4.3.1)
 deldir                 1.0-9       2023-05-17 [2] RSPM (R 4.3.0)
 digest                 0.6.33      2023-07-07 [2] RSPM (R 4.3.0)
 dplyr                * 1.1.2       2023-04-20 [2] RSPM (R 4.3.0)
 effectsize             0.8.5       2023-08-09 [2] RSPM (R 4.3.0)
 ellipsis               0.3.2       2021-04-29 [2] RSPM (R 4.3.0)
 emmeans                1.8.7       2023-06-23 [2] RSPM (R 4.3.0)
 entropy                1.3.1       2021-10-02 [2] RSPM (R 4.3.0)
 estimability           1.4.1       2022-08-05 [2] RSPM (R 4.3.0)
 evaluate               0.21        2023-05-05 [2] RSPM (R 4.3.0)
 fansi                  1.0.4       2023-01-22 [2] RSPM (R 4.3.0)
 farver                 2.1.1       2022-07-06 [2] RSPM (R 4.3.0)
 fastmap                1.1.1       2023-02-24 [2] RSPM (R 4.3.0)
 fitdistrplus           1.1-11      2023-04-25 [2] RSPM (R 4.3.0)
 forcats              * 1.0.0       2023-01-29 [2] RSPM (R 4.3.0)
 future               * 1.33.0      2023-07-01 [2] RSPM (R 4.3.0)
 future.apply           1.11.0      2023-05-21 [2] RSPM (R 4.3.0)
 generics               0.1.3       2022-07-05 [2] RSPM (R 4.3.0)
 GenomeInfoDb         * 1.36.1      2023-06-21 [2] RSPM (R 4.3.1)
 GenomeInfoDbData       1.2.10      2023-08-17 [2] RSPM (R 4.3.1)
 GenomicRanges        * 1.52.0      2023-04-25 [2] RSPM (R 4.3.1)
 ggbeeswarm             0.7.2       2023-08-17 [2] Github (eclarke/ggbeeswarm@3cf58a9)
 ggforce                0.4.1.9000  2023-08-17 [2] Github (thomasp85/ggforce@9be635c)
 ggmin                  0.0.0.9000  2023-08-17 [2] Github (sjessa/ggmin@8ada274)
 ggplot2              * 3.4.3       2023-08-14 [2] RSPM (R 4.3.0)
 ggprism                1.0.4       2023-08-17 [2] Github (csdaw/ggprism@0e411f4)
 ggrastr                1.0.2       2023-08-17 [2] Github (VPetukhov/ggrastr@50ca3e0)
 ggrepel                0.9.3       2023-08-17 [2] Github (slowkow/ggrepel@4584949)
 ggridges               0.5.4       2022-09-26 [2] RSPM (R 4.3.0)
 ggstatsplot          * 0.12.0.9000 2023-08-17 [2] Github (IndrajeetPatil/ggstatsplot@4a29843)
 GlobalOptions          0.1.2       2020-06-10 [2] RSPM (R 4.3.0)
 globals                0.16.2      2022-11-21 [2] RSPM (R 4.3.0)
 glue                   1.6.2       2022-02-24 [2] RSPM (R 4.3.0)
 goftest                1.2-3       2021-10-07 [2] RSPM (R 4.3.0)
 gridExtra              2.3         2017-09-09 [2] RSPM (R 4.3.0)
 gtable                 0.3.3       2023-03-21 [2] RSPM (R 4.3.0)
 hdf5r                  1.3.8       2023-01-21 [2] RSPM (R 4.3.1)
 here                 * 1.0.1       2020-12-13 [2] RSPM (R 4.3.0)
 hexbin                 1.28.3      2023-03-21 [2] RSPM (R 4.3.0)
 hms                    1.1.3       2023-03-21 [2] RSPM (R 4.3.0)
 htmltools              0.5.6       2023-08-10 [2] RSPM (R 4.3.0)
 htmlwidgets            1.6.2       2023-03-17 [2] RSPM (R 4.3.0)
 httpuv                 1.6.11      2023-05-11 [2] RSPM (R 4.3.0)
 httr                   1.4.7       2023-08-15 [2] RSPM (R 4.3.0)
 ica                    1.0-3       2022-07-08 [2] RSPM (R 4.3.0)
 igraph                 1.5.1       2023-08-10 [2] RSPM (R 4.3.0)
 insight                0.19.3.2    2023-08-17 [2] https://easystats.r-universe.dev (R 4.3.1)
 IRanges              * 2.34.1      2023-06-22 [2] RSPM (R 4.3.1)
 irlba                  2.3.5.1     2022-10-03 [2] RSPM (R 4.3.0)
 janitor                2.2.0.9000  2023-08-17 [2] Github (sfirke/janitor@b2700cf)
 jsonlite               1.8.7       2023-06-29 [2] RSPM (R 4.3.0)
 KernSmooth             2.23-22     2023-07-10 [2] RSPM (R 4.3.0)
 knitr                  1.43        2023-05-25 [2] RSPM (R 4.3.0)
 ks                     1.14.1      2023-08-10 [2] RSPM (R 4.3.0)
 labeling               0.4.2       2020-10-20 [2] RSPM (R 4.3.0)
 later                  1.3.1       2023-05-02 [2] RSPM (R 4.3.0)
 lattice                0.21-8      2023-04-05 [2] RSPM (R 4.3.0)
 lazyeval               0.2.2       2019-03-15 [2] RSPM (R 4.3.0)
 leiden                 0.4.3       2022-09-10 [2] RSPM (R 4.3.0)
 lifecycle              1.0.3       2022-10-07 [2] RSPM (R 4.3.0)
 listenv                0.9.0       2022-12-16 [2] RSPM (R 4.3.0)
 lmtest                 0.9-40      2022-03-21 [2] RSPM (R 4.3.0)
 lubridate            * 1.9.2       2023-02-10 [2] RSPM (R 4.3.0)
 magrittr             * 2.0.3       2022-03-30 [2] RSPM (R 4.3.0)
 MASS                   7.3-60      2023-05-04 [2] CRAN (R 4.3.1)
 Matrix                 1.6-1       2023-08-14 [2] RSPM (R 4.3.0)
 MatrixGenerics       * 1.12.3      2023-07-30 [2] RSPM (R 4.3.1)
 MatrixModels           0.5-2       2023-07-10 [2] RSPM (R 4.3.0)
 matrixStats          * 1.0.0       2023-06-02 [2] RSPM (R 4.3.0)
 mclust                 6.0.0       2022-10-31 [2] RSPM (R 4.3.0)
 mime                   0.12        2021-09-28 [2] RSPM (R 4.3.0)
 miniUI                 0.1.1.1     2018-05-18 [2] RSPM (R 4.3.0)
 multcomp               1.4-25      2023-06-20 [2] RSPM (R 4.3.0)
 munsell                0.5.0       2018-06-12 [2] RSPM (R 4.3.0)
 mvtnorm                1.2-2       2023-06-08 [2] RSPM (R 4.3.0)
 Nebulosa               1.10.0      2023-04-25 [2] RSPM (R 4.3.1)
 nlme                   3.1-163     2023-08-09 [2] RSPM (R 4.3.0)
 paletteer              1.5.0       2022-10-19 [2] RSPM (R 4.3.0)
 parallelly             1.36.0      2023-05-26 [2] RSPM (R 4.3.0)
 parameters             0.21.1      2023-05-26 [2] RSPM (R 4.3.0)
 patchwork            * 1.1.3.9000  2023-08-17 [2] Github (thomasp85/patchwork@51a6eff)
 pbapply                1.7-2       2023-06-27 [2] RSPM (R 4.3.0)
 pillar                 1.9.0       2023-03-22 [2] RSPM (R 4.3.0)
 pkgconfig              2.0.3       2019-09-22 [2] RSPM (R 4.3.0)
 plotly                 4.10.2      2023-06-03 [2] RSPM (R 4.3.0)
 plyr                   1.8.8       2022-11-11 [2] RSPM (R 4.3.0)
 png                    0.1-8       2022-11-29 [2] RSPM (R 4.3.0)
 polyclip               1.10-4      2022-10-20 [2] RSPM (R 4.3.0)
 pracma                 2.4.2       2022-09-22 [2] RSPM (R 4.3.0)
 prismatic              1.1.1       2022-08-15 [2] RSPM (R 4.3.0)
 progressr              0.14.0      2023-08-10 [2] RSPM (R 4.3.0)
 promises               1.2.1       2023-08-10 [2] RSPM (R 4.3.0)
 purrr                * 1.0.2       2023-08-10 [2] RSPM (R 4.3.0)
 R.methodsS3            1.8.2       2022-06-13 [2] RSPM (R 4.3.0)
 R.oo                   1.25.0      2022-06-12 [2] RSPM (R 4.3.0)
 R.utils                2.12.2      2022-11-11 [2] RSPM (R 4.3.0)
 R6                     2.5.1       2021-08-19 [2] RSPM (R 4.3.0)
 RANN                   2.6.1       2019-01-08 [2] RSPM (R 4.3.0)
 RColorBrewer         * 1.1-3       2022-04-03 [2] RSPM (R 4.3.0)
 Rcpp                   1.0.11      2023-07-06 [2] RSPM (R 4.3.0)
 RcppAnnoy              0.0.21      2023-07-02 [2] RSPM (R 4.3.0)
 RCurl                  1.98-1.12   2023-03-27 [2] RSPM (R 4.3.0)
 readr                * 2.1.4       2023-02-10 [2] RSPM (R 4.3.0)
 rematch2               2.1.2       2020-05-01 [2] RSPM (R 4.3.0)
 remotes                2.4.2.1     2023-07-18 [2] RSPM (R 4.3.0)
 repr                   1.1.6       2023-01-26 [2] RSPM (R 4.3.0)
 reshape2               1.4.4       2020-04-09 [2] RSPM (R 4.3.0)
 reticulate           * 1.31        2023-08-10 [2] RSPM (R 4.3.0)
 rlang                  1.1.1       2023-04-28 [2] RSPM (R 4.3.0)
 rmarkdown              2.24        2023-08-14 [2] RSPM (R 4.3.0)
 ROCR                   1.0-11      2020-05-02 [2] RSPM (R 4.3.0)
 rprojroot              2.0.3       2022-04-02 [2] RSPM (R 4.3.0)
 rstudioapi             0.15.0      2023-07-07 [2] RSPM (R 4.3.0)
 rsvd                   1.0.5       2021-04-16 [2] RSPM (R 4.3.0)
 Rtsne                  0.16        2022-04-17 [2] RSPM (R 4.3.0)
 S4Arrays               1.0.5       2023-07-24 [2] RSPM (R 4.3.1)
 S4Vectors            * 0.38.1      2023-05-02 [2] RSPM (R 4.3.1)
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 scattermore            1.2         2023-06-12 [2] RSPM (R 4.3.0)
 scCustomize          * 1.1.3       2023-08-17 [2] Github (samuel-marsh/scCustomize@2bb2b13)
 sceasy               * 0.0.7       2023-08-17 [2] Github (cellgeni/sceasy@0cfc0e3)
 schex                * 1.14.0      2023-04-25 [2] RSPM (R 4.3.1)
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 Seurat               * 4.3.0       2022-11-18 [2] RSPM (R 4.3.1)
 SeuratDisk           * 0.0.0.9020  2023-08-17 [2] Github (mojaveazure/seurat-disk@9b89970)
 SeuratObject         * 4.1.3       2022-11-07 [2] RSPM (R 4.3.0)
 SeuratWrappers       * 0.3.1       2023-08-17 [2] Github (satijalab/seurat-wrappers@d28512f)
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 shiny                * 1.7.5       2023-08-12 [2] RSPM (R 4.3.0)
 SingleCellExperiment * 1.22.0      2023-04-25 [2] RSPM (R 4.3.1)
 skimr                * 2.1.5       2023-08-17 [2] Github (ropensci/skimr@d5126aa)
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 spatstat.explore       3.2-1       2023-05-13 [2] RSPM (R 4.3.0)
 spatstat.geom          3.2-4       2023-07-20 [2] RSPM (R 4.3.0)
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 stringr              * 1.5.0       2022-12-02 [2] RSPM (R 4.3.0)
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 xtable                 1.8-4       2019-04-21 [2] RSPM (R 4.3.0)
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 [1] /home/etretiakov/R/x86_64-pc-linux-gnu-library/4.3
 [2] /opt/R/4.3.1/lib/R/library

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