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

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# 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(
  "Insr", "Meis1", "Igf1r", "Nrf1", "Prlr", "Camk1d", "Lars2", "Cacna2d3", # 0 ARC
  "Dcn", "Ptgds", "Nupr1", "Igfbp2", "Nrarp", "Ctnna2", "Ldlr", "Mmp14", # 1 LHA
  "Nkx6-2", "Cirbp", "Selenop", # 2 MnPO
  "Foxg1", "Crym", "Sema3c", "Meis2", "Dbp", "Drd1", "Drd2", "Drd3", "Drd4", "Drd5", # 3 POA
  "Egr1", "Ttr", "Btg2", "Mbnl3", "Pgf", "Irs4", "Gpr101", "Nr3c2", "Agtr1", # 4 PVN
  "Rfx4", "Dbx2", "Prokr2", "Cebpb", "Zic1", "Zic2", "Ccn1", "Gata4", "Klf10", # 5 SCN
  "Tbx3", "Fis1", "Ndn", "Psmc5", "Drap1", "Pcsk1n", "Rtn1", # 6 VMH
  "Emx2", "Sgip1", "Myoc", "Hivep3", "Dcc", "Ltbp1", "Ralyl", "Egfem1", # 7 VPH
  "Klf4", "Atf3", "Nrg1", "Cdk8", "Pitx2", "Otx1", "Otx2", "Peg3", "Isl1", "Myt1l", "Nr5a1", "Otp", "Six6", "Nkx2-1", "Qrfpr", "Hcrtr1", "Hcrtr2", "Tacr1", "Trhr", "Tshr", "Grpr", "Npy1r", "Npy2r", "Npy5r",
  "Gfap", "Fgf1", "Fgfr3", "Hepacam", "Hif1", "Htra1", "Lxn", "Ndrg2", "Ntn1", "Nfia", "Slit2", "Aqp4", "S100a1", "S100a6", "S100b", "Slc1a2", "Slc1a3", "Slc38a1", "Vegfa", "Fos", "Fosb", "Jun", "Junb", "Jund", "Ier2", "Socs3", "Pde10a", "Fbln5"
)


# Iterate_FeaturePlot_scCustom(
#   seurat_object = srt,
#   gene_list = selected_genes,
#   reduction = "densmap_xgboost",
#   na_cutoff = NA,
#   pt.size = 4,
#   order = TRUE,
#   raster = TRUE,
#   raster.dpi = c(960, 960),
#   alpha_na_exp = 0.1,
#   alpha_exp = 0.75,
#   single_pdf = FALSE,
#   file_path = plots_dir,
#   file_type = ".pdf",
#   file_name = sprintf("%s_selected", bioproject),
#   colors_use = srt@misc$expr_Colour_Palette_gnuplot_r
# )

# Iterate_FeaturePlot_scCustom(
#   seurat_object = srt,
#   gene_list = gene_int[gene_int %in% all.genes],
#   reduction = "densmap_xgboost",
#   na_cutoff = NA,
#   pt.size = 4,
#   order = TRUE,
#   raster = TRUE,
#   raster.dpi = c(960, 960),
#   alpha_na_exp = 0.1,
#   alpha_exp = 0.75,
#   single_pdf = FALSE,
#   file_path = plots_dir,
#   file_type = ".pdf",
#   file_name = sprintf("%s_selected", bioproject),
#   colors_use = srt@misc$expr_Colour_Palette_gnuplot_r
# )

Code

Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Insr", "Lxn"))

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Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Gfap", "Lxn"))

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Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Insr", "Gfap"))

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Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Gfap", "Mfn2"))

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Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Lxn", "Mfn2"))

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

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

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

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Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Gfap", "Mfn2"), custom_palette = srt@misc$div_Colour_Pal)

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Plot_Density_Joint_Only(seurat_object = srt, reduction = "densmap_xgboost", features = c("Lxn", "Mfn2"), custom_palette = srt@misc$div_Colour_Pal)

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sbs_mtx <- GetAssayData(object = srt, slot = "count", assay = "RNA") %>%
  as.data.frame() %>%
  t() %>%
  as.data.frame() %>%
  select(Insr, Lxn) %>%
  dplyr::bind_cols(srt@meta.data) %>%
  select(region, Insr, Lxn) %>%
  mutate(Insr_pos = Insr > 0,
         Lxn_pos  = Lxn  > 0)

sbs_mtx %>% skimr::skim()

Data summary

Name	Piped data
Number of rows	35642
Number of columns	5
_______________________
Column type frequency:
factor	1
logical	2
numeric	2
________________________
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

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
Insr_pos	0	1	0.24	FAL: 27084, TRU: 8558
Lxn_pos	0	1	0.35	FAL: 23165, TRU: 12477

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Insr	0	1	0.31	0.64	0	0	0	0	8	▇▁▁▁▁
Lxn	0	1	0.57	1.19	0	0	0	1	31	▇▁▁▁▁

Code

# plot
grouped_ggpiestats(
  # arguments relevant for `ggpiestats()`
  data = sbs_mtx,
  x = Insr_pos,
  y = Lxn_pos,
  grouping.var = region,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "InsR specification of Lxn-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 = Insr,
  y = Lxn,
  grouping.var = region,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "InsR specification of Lxn-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

srt <- make_hexbin(
  srt,
  nbins = 64,
  dimension_reduction = "densmap_xgboost",
  use_dims = c(1, 2)
)

plot_hexbin_density(srt) + ggsci::scale_fill_material("amber")

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plot_hexbin_meta(
  srt,
  col = "project",
  action = "majority"
)

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plot_hexbin_meta(
  srt,
  col = "orig.ident",
  action = "majority"
)

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plot_hexbin_meta(
  srt,
  col = "tech",
  action = "majority"
)

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plot_hexbin_meta(
  srt,
  col = "sex",
  action = "majority"
)

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plot_hexbin_meta(
  srt,
  col = "stage",
  action = "majority"
)

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plot_hexbin_meta(
  srt,
  col = "predictions_xgboost_eval",
  action = "majority",
  colors = colours_code
)

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plot_hexbin_meta(
  srt,
  col = "region",
  action = "majority",
  colors = colours_region
)

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plot_custom_median_hex <- function(gene_id) {
  plot_hexbin_feature_plus(srt,
    col = "region",
    colors = colours_region,
    feature = gene_id,
    mod = "RNA",
    type = "data",
    action = "median",
    xlab = "XGBoost_densMAP_1", ylab = "XGBoost_densMAP_2",
    title = sprintf("Median of mRNA expression of %s in logarithmic scale", gene_id),
  ) + scale_fill_gradientn(
    colors = srt@misc$expr_Colour_Palette_gnuplot_r,
    na.value = "white"
  )
}

plot_custom_mean_hex <- function(gene_id) {
  plot_hexbin_feature_plus(srt,
    col = "region",
    colors = colours_region,
    feature = gene_id,
    mod = "RNA",
    type = "data",
    action = "mean",
    xlab = "XGBoost_densMAP_1", ylab = "XGBoost_densMAP_2",
    title = sprintf("Mean of mRNA expression of %s in logarithmic scale", gene_id),
  ) + scale_fill_gradientn(
    colors = srt@misc$expr_Colour_Palette_gnuplot_r,
    na.value = "white"
  )
}

map(selected_genes[selected_genes %in% rownames(GetAssayData(object = srt, slot = "data"))], plot_custom_median_hex)

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map(selected_genes[selected_genes %in% rownames(GetAssayData(object = srt, slot = "data"))], plot_custom_mean_hex)

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map(gene_int[gene_int %in% rownames(GetAssayData(object = srt, slot = "data"))], plot_custom_median_hex)

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Code

map(gene_int[gene_int %in% rownames(GetAssayData(object = srt, slot = "data"))], plot_custom_mean_hex)

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Session information

Code

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

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.2 (2022-10-31)
 os       Ubuntu 22.04.2 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     2023-07-25
 pandoc   2.19.2 @ /opt/python/3.8.8/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version     date (UTC) lib source
 abind                  1.4-5       2016-07-21 [1] RSPM (R 4.2.0)
 base64enc              0.1-3       2015-07-28 [1] RSPM (R 4.2.0)
 BayesFactor            0.9.12-4.4  2022-07-05 [1] RSPM (R 4.2.0)
 bayestestR             0.13.1      2023-04-30 [1] https://easystats.r-universe.dev (R 4.2.2)
 beeswarm               0.4.0       2021-06-01 [1] RSPM (R 4.2.0)
 Biobase              * 2.58.0      2022-11-01 [1] RSPM (R 4.2.2)
 BiocGenerics         * 0.44.0      2022-11-01 [1] RSPM (R 4.2.2)
 BiocManager            1.30.20     2023-02-24 [1] RSPM (R 4.2.0)
 bit                    4.0.5       2022-11-15 [1] RSPM (R 4.2.0)
 bit64                  4.0.5       2020-08-30 [1] RSPM (R 4.2.0)
 bitops                 1.0-7       2021-04-24 [1] RSPM (R 4.2.0)
 circlize               0.4.15      2022-05-10 [1] RSPM (R 4.2.0)
 cli                    3.6.1       2023-03-23 [1] RSPM (R 4.2.0)
 cluster                2.1.4       2022-08-22 [1] RSPM (R 4.2.0)
 coda                   0.19-4      2020-09-30 [1] RSPM (R 4.2.0)
 codetools              0.2-19      2023-02-01 [1] RSPM (R 4.2.0)
 colorspace             2.1-0       2023-01-23 [1] RSPM (R 4.2.0)
 concaveman             1.1.0       2020-05-11 [1] RSPM (R 4.2.0)
 correlation            0.8.4       2023-04-30 [1] Github (easystats/correlation@3d5cd1e)
 cowplot              * 1.1.1       2020-12-30 [1] RSPM (R 4.2.0)
 crayon                 1.5.2       2022-09-29 [1] RSPM (R 4.2.0)
 curl                   5.0.0       2023-01-12 [1] RSPM (R 4.2.0)
 data.table             1.14.8      2023-02-17 [1] RSPM (R 4.2.0)
 datawizard             0.7.1       2023-04-03 [1] RSPM (R 4.2.0)
 DelayedArray           0.24.0      2022-11-01 [1] RSPM (R 4.2.2)
 deldir                 1.0-6       2021-10-23 [1] RSPM (R 4.2.0)
 digest                 0.6.31      2022-12-11 [1] RSPM (R 4.2.0)
 dplyr                * 1.1.2       2023-04-20 [1] RSPM (R 4.2.2)
 effectsize             0.8.3       2023-01-28 [1] RSPM (R 4.2.0)
 ellipsis               0.3.2       2021-04-29 [1] RSPM (R 4.2.0)
 emmeans                1.8.5       2023-03-08 [1] RSPM (R 4.2.0)
 entropy                1.3.1       2021-10-02 [1] RSPM (R 4.2.0)
 estimability           1.4.1       2022-08-05 [1] RSPM (R 4.2.0)
 evaluate               0.20        2023-01-17 [1] RSPM (R 4.2.0)
 fansi                  1.0.4       2023-01-22 [1] RSPM (R 4.2.0)
 farver                 2.1.1       2022-07-06 [1] RSPM (R 4.2.0)
 fastmap                1.1.1       2023-02-24 [1] RSPM (R 4.2.0)
 fitdistrplus           1.1-11      2023-04-25 [1] RSPM (R 4.2.2)
 forcats              * 1.0.0       2023-01-29 [1] RSPM (R 4.2.0)
 future               * 1.32.0      2023-03-07 [1] RSPM (R 4.2.0)
 future.apply           1.10.0      2022-11-05 [1] RSPM (R 4.2.0)
 generics               0.1.3       2022-07-05 [1] RSPM (R 4.2.0)
 GenomeInfoDb         * 1.34.9      2023-02-02 [1] RSPM (R 4.2.2)
 GenomeInfoDbData       1.2.9       2023-04-30 [1] RSPM (R 4.2.2)
 GenomicRanges        * 1.50.2      2022-12-16 [1] RSPM (R 4.2.2)
 ggbeeswarm             0.7.2       2023-04-30 [1] Github (eclarke/ggbeeswarm@3cf58a9)
 ggforce                0.4.1.9000  2023-04-30 [1] Github (thomasp85/ggforce@9be635c)
 ggmin                  0.0.0.9000  2023-04-30 [1] Github (sjessa/ggmin@8ada274)
 ggplot2              * 3.4.2       2023-04-03 [1] RSPM (R 4.2.0)
 ggprism                1.0.4       2023-04-30 [1] Github (csdaw/ggprism@0e411f4)
 ggrastr                1.0.1       2023-04-30 [1] Github (VPetukhov/ggrastr@7aed9af)
 ggrepel                0.9.2.9999  2023-04-30 [1] Github (slowkow/ggrepel@fe3b5c3)
 ggridges               0.5.4       2022-09-26 [1] RSPM (R 4.2.0)
 ggsci                  3.0.0       2023-04-30 [1] Github (nanxstats/ggsci@028b373)
 ggstatsplot          * 0.11.1.9000 2023-04-30 [1] Github (IndrajeetPatil/ggstatsplot@befe812)
 GlobalOptions          0.1.2       2020-06-10 [1] RSPM (R 4.2.0)
 globals                0.16.2      2022-11-21 [1] RSPM (R 4.2.0)
 glue                   1.6.2       2022-02-24 [1] RSPM (R 4.2.0)
 goftest                1.2-3       2021-10-07 [1] RSPM (R 4.2.0)
 gridExtra              2.3         2017-09-09 [1] RSPM (R 4.2.0)
 gtable                 0.3.3       2023-03-21 [1] RSPM (R 4.2.0)
 hdf5r                  1.3.8       2023-01-21 [1] RSPM (R 4.2.2)
 here                 * 1.0.1       2020-12-13 [1] RSPM (R 4.2.0)
 hexbin                 1.28.3      2023-03-21 [1] RSPM (R 4.2.0)
 hms                    1.1.3       2023-03-21 [1] RSPM (R 4.2.0)
 htmltools              0.5.5       2023-03-23 [1] RSPM (R 4.2.0)
 htmlwidgets            1.6.2       2023-03-17 [1] RSPM (R 4.2.0)
 httpuv                 1.6.9       2023-02-14 [1] RSPM (R 4.2.0)
 httr                   1.4.5       2023-02-24 [1] RSPM (R 4.2.0)
 ica                    1.0-3       2022-07-08 [1] RSPM (R 4.2.0)
 igraph                 1.4.1       2023-02-24 [1] RSPM (R 4.2.0)
 insight                0.19.1      2023-03-18 [1] RSPM (R 4.2.0)
 IRanges              * 2.32.0      2022-11-01 [1] RSPM (R 4.2.2)
 irlba                  2.3.5.1     2022-10-03 [1] RSPM (R 4.2.0)
 janitor                2.2.0.9000  2023-04-30 [1] Github (sfirke/janitor@d64c8bb)
 jsonlite               1.8.4       2022-12-06 [1] RSPM (R 4.2.0)
 KernSmooth             2.23-20     2021-05-03 [1] RSPM (R 4.2.0)
 knitr                  1.42        2023-01-25 [1] RSPM (R 4.2.0)
 ks                     1.14.0      2022-11-24 [1] RSPM (R 4.2.0)
 labeling               0.4.2       2020-10-20 [1] RSPM (R 4.2.0)
 later                  1.3.0       2021-08-18 [1] RSPM (R 4.2.0)
 lattice                0.21-8      2023-04-05 [1] RSPM (R 4.2.0)
 lazyeval               0.2.2       2019-03-15 [1] RSPM (R 4.2.0)
 leiden                 0.4.3       2022-09-10 [1] RSPM (R 4.2.0)
 lifecycle              1.0.3       2022-10-07 [1] RSPM (R 4.2.0)
 listenv                0.9.0       2022-12-16 [1] RSPM (R 4.2.0)
 lmtest                 0.9-40      2022-03-21 [1] RSPM (R 4.2.0)
 lubridate            * 1.9.2       2023-02-10 [1] RSPM (R 4.2.0)
 magrittr             * 2.0.3       2022-03-30 [1] RSPM (R 4.2.0)
 MASS                   7.3-58.1    2022-08-03 [1] CRAN (R 4.2.2)
 Matrix                 1.5-4       2023-04-04 [1] RSPM (R 4.2.0)
 MatrixGenerics       * 1.10.0      2022-11-01 [1] RSPM (R 4.2.2)
 MatrixModels           0.5-1       2022-09-11 [1] RSPM (R 4.2.0)
 matrixStats          * 0.63.0      2022-11-18 [1] RSPM (R 4.2.0)
 mclust                 6.0.0       2022-10-31 [1] RSPM (R 4.2.0)
 mime                   0.12        2021-09-28 [1] RSPM (R 4.2.0)
 miniUI                 0.1.1.1     2018-05-18 [1] RSPM (R 4.2.0)
 multcomp               1.4-23      2023-03-09 [1] RSPM (R 4.2.0)
 munsell                0.5.0       2018-06-12 [1] RSPM (R 4.2.0)
 mvtnorm                1.1-3       2021-10-08 [1] RSPM (R 4.2.0)
 Nebulosa               1.8.0       2022-11-01 [1] RSPM (R 4.2.2)
 nlme                   3.1-162     2023-01-31 [1] RSPM (R 4.2.0)
 paletteer              1.5.0       2022-10-19 [1] RSPM (R 4.2.0)
 parallelly             1.35.0      2023-03-23 [1] RSPM (R 4.2.0)
 parameters             0.20.3      2023-04-05 [1] RSPM (R 4.2.0)
 patchwork            * 1.1.2.9000  2023-04-30 [1] Github (thomasp85/patchwork@c14c960)
 pbapply                1.7-0       2023-01-13 [1] RSPM (R 4.2.0)
 pillar                 1.9.0       2023-03-22 [1] RSPM (R 4.2.0)
 pkgconfig              2.0.3       2019-09-22 [1] RSPM (R 4.2.0)
 plotly                 4.10.1      2022-11-07 [1] RSPM (R 4.2.0)
 plyr                   1.8.8       2022-11-11 [1] RSPM (R 4.2.0)
 png                    0.1-8       2022-11-29 [1] RSPM (R 4.2.0)
 polyclip               1.10-4      2022-10-20 [1] RSPM (R 4.2.0)
 pracma                 2.4.2       2022-09-22 [1] RSPM (R 4.2.0)
 prismatic              1.1.1       2022-08-15 [1] RSPM (R 4.2.0)
 progressr              0.13.0      2023-01-10 [1] RSPM (R 4.2.0)
 promises               1.2.0.1     2021-02-11 [1] RSPM (R 4.2.0)
 purrr                * 1.0.1       2023-01-10 [1] RSPM (R 4.2.0)
 R.methodsS3            1.8.2       2022-06-13 [1] RSPM (R 4.2.0)
 R.oo                   1.25.0      2022-06-12 [1] RSPM (R 4.2.0)
 R.utils                2.12.2      2022-11-11 [1] RSPM (R 4.2.0)
 R6                     2.5.1       2021-08-19 [1] RSPM (R 4.2.0)
 RANN                   2.6.1       2019-01-08 [1] RSPM (R 4.2.0)
 RColorBrewer         * 1.1-3       2022-04-03 [1] RSPM (R 4.2.0)
 Rcpp                   1.0.10      2023-01-22 [1] RSPM (R 4.2.0)
 RcppAnnoy              0.0.20      2022-10-27 [1] RSPM (R 4.2.0)
 RCurl                  1.98-1.12   2023-03-27 [1] RSPM (R 4.2.0)
 readr                * 2.1.4       2023-02-10 [1] RSPM (R 4.2.0)
 rematch2               2.1.2       2020-05-01 [1] RSPM (R 4.2.0)
 remotes                2.4.2       2021-11-30 [1] RSPM (R 4.2.0)
 repr                   1.1.6       2023-01-26 [1] RSPM (R 4.2.0)
 reshape2               1.4.4       2020-04-09 [1] RSPM (R 4.2.0)
 reticulate           * 1.28-9000   2023-04-30 [1] Github (rstudio/reticulate@442c49f)
 rlang                  1.1.0       2023-03-14 [1] RSPM (R 4.2.0)
 rmarkdown              2.21        2023-03-26 [1] RSPM (R 4.2.0)
 ROCR                   1.0-11      2020-05-02 [1] RSPM (R 4.2.0)
 rprojroot              2.0.3       2022-04-02 [1] RSPM (R 4.2.0)
 rsvd                   1.0.5       2021-04-16 [1] RSPM (R 4.2.0)
 Rtsne                  0.16        2022-04-17 [1] RSPM (R 4.2.0)
 S4Vectors            * 0.36.2      2023-02-26 [1] RSPM (R 4.2.2)
 sandwich               3.0-2       2022-06-15 [1] RSPM (R 4.2.0)
 scales                 1.2.1       2022-08-20 [1] RSPM (R 4.2.0)
 scattermore            0.8         2022-02-14 [1] RSPM (R 4.2.0)
 scCustomize          * 1.1.1       2023-04-30 [1] Github (samuel-marsh/scCustomize@d08268d)
 sceasy               * 0.0.7       2023-04-30 [1] Github (cellgeni/sceasy@0cfc0e3)
 schex                * 1.12.0      2022-11-01 [1] RSPM (R 4.2.2)
 sctransform            0.3.5       2022-09-21 [1] RSPM (R 4.2.0)
 sessioninfo            1.2.2       2021-12-06 [1] RSPM (R 4.2.0)
 Seurat               * 4.3.0       2022-11-18 [1] RSPM (R 4.2.2)
 SeuratDisk           * 0.0.0.9020  2023-04-30 [1] Github (mojaveazure/seurat-disk@9b89970)
 SeuratObject         * 4.1.3       2022-11-07 [1] RSPM (R 4.2.0)
 SeuratWrappers       * 0.3.1       2023-04-30 [1] Github (satijalab/seurat-wrappers@d28512f)
 shape                  1.4.6       2021-05-19 [1] RSPM (R 4.2.0)
 shiny                * 1.7.4       2022-12-15 [1] RSPM (R 4.2.0)
 SingleCellExperiment * 1.20.1      2023-03-17 [1] RSPM (R 4.2.2)
 skimr                * 2.1.5       2023-04-30 [1] Github (ropensci/skimr@d5126aa)
 snakecase              0.11.0      2019-05-25 [1] RSPM (R 4.2.0)
 sp                     1.6-0       2023-01-19 [1] RSPM (R 4.2.0)
 spatstat.data          3.0-1       2023-03-12 [1] RSPM (R 4.2.0)
 spatstat.explore       3.1-0       2023-03-14 [1] RSPM (R 4.2.0)
 spatstat.geom          3.1-0       2023-03-12 [1] RSPM (R 4.2.0)
 spatstat.random        3.1-4       2023-03-13 [1] RSPM (R 4.2.0)
 spatstat.sparse        3.0-1       2023-03-12 [1] RSPM (R 4.2.0)
 spatstat.utils         3.0-2       2023-03-11 [1] RSPM (R 4.2.0)
 statsExpressions       1.5.0       2023-02-19 [1] RSPM (R 4.2.0)
 stringi                1.7.12      2023-01-11 [1] RSPM (R 4.2.0)
 stringr              * 1.5.0       2022-12-02 [1] RSPM (R 4.2.0)
 SummarizedExperiment * 1.28.0      2022-11-01 [1] RSPM (R 4.2.2)
 survival               3.5-5       2023-03-12 [1] RSPM (R 4.2.0)
 systemfonts            1.0.4       2022-02-11 [1] RSPM (R 4.2.0)
 tensor                 1.5         2012-05-05 [1] RSPM (R 4.2.0)
 TH.data                1.1-1       2022-04-26 [1] RSPM (R 4.2.0)
 tibble               * 3.2.1       2023-03-20 [1] RSPM (R 4.2.0)
 tidyr                * 1.3.0       2023-01-24 [1] RSPM (R 4.2.0)
 tidyselect             1.2.0       2022-10-10 [1] RSPM (R 4.2.0)
 tidyverse            * 2.0.0.9000  2023-04-30 [1] Github (tidyverse/tidyverse@8ec2e1f)
 timechange             0.2.0       2023-01-11 [1] RSPM (R 4.2.0)
 tweenr                 2.0.2       2022-09-06 [1] RSPM (R 4.2.0)
 tzdb                   0.3.0       2022-03-28 [1] RSPM (R 4.2.0)
 utf8                   1.2.3       2023-01-31 [1] RSPM (R 4.2.0)
 uwot                   0.1.14      2022-08-22 [1] RSPM (R 4.2.0)
 V8                     4.2.2       2022-11-03 [1] RSPM (R 4.2.0)
 vctrs                  0.6.2       2023-04-19 [1] RSPM (R 4.2.2)
 vipor                  0.4.5       2017-03-22 [1] RSPM (R 4.2.0)
 viridis              * 0.6.2       2021-10-13 [1] RSPM (R 4.2.0)
 viridisLite          * 0.4.1       2022-08-22 [1] RSPM (R 4.2.0)
 vroom                  1.6.1       2023-01-22 [1] RSPM (R 4.2.0)
 withr                  2.5.0       2022-03-03 [1] RSPM (R 4.2.0)
 xfun                   0.39        2023-04-20 [1] RSPM (R 4.2.2)
 xtable                 1.8-4       2019-04-21 [1] RSPM (R 4.2.0)
 XVector                0.38.0      2022-11-01 [1] RSPM (R 4.2.2)
 yaml                   2.3.7       2023-01-23 [1] RSPM (R 4.2.0)
 zeallot                0.1.0       2018-01-28 [1] RSPM (R 4.2.0)
 zlibbioc               1.44.0      2022-11-01 [1] RSPM (R 4.2.2)
 zoo                    1.8-12      2023-04-13 [1] RSPM (R 4.2.2)

 [1] /opt/R/4.2.2/lib/R/library

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