Last updated: 2023-03-13

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Load dataset

srt <- LoadH5Seurat(here(data_dir, "Pr5P7_clusters.h5Seurat"))
Idents(srt) <- "sep_clstr"
neurons <- subset(srt, subset = Rbfox3 > 0 | Elavl4 > 0 | Snap25 > 0 | Stmn2 > 0)
gneurons <- subset(neurons, subset = Galr1 > 0)

We check list of genes based on our prior knowledge.

gene_of_interest <-
  c("Galr1", "Galr2", "Galr3", 
    "Ntn1", "Ntn2", "Ntn3", "Ntn4", "Ntn5", 
    "Slit1", "Dcc",
    "Prkaca", "Adcy1", "Grin1", 
    "Oxt", "Npy", "Sst", "Avp")

genes_present <- Gene_Present(data = srt, gene_list = gene_of_interest)
genes_present
$found_features
 [1] "Galr1"  "Galr2"  "Ntn1"   "Ntn3"   "Ntn4"   "Slit1"  "Dcc"    "Prkaca"
 [9] "Adcy1"  "Grin1"  "Npy"    "Sst"   

$bad_features
[1] "Galr3" "Ntn2"  "Ntn5"  "Oxt"   "Avp"  

$wrong_case_found_features
[1] "NA (check not performed.  Set 'case_check = TRUE' to perform check."
DotPlot_scCustom(
  seurat_object = srt,
  assay = "RNA",
  features = genes_present$found_features,
  flip_axes = TRUE,
  x_lab_rotate = TRUE,
  colors_use = viridis(n = 30, alpha = .75, direction = -1, option = "E"))

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
DimPlot_scCustom(
  srt,
  label = TRUE,
  repel = TRUE,
  pt.size = 2,
  figure_plot = TRUE
  ) + ggtitle("Clusters") + NoLegend()

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
sbs_mtx_neuro <-
    neurons %>%
    GetAssayData("data", "RNA") %>%
    as.data.frame() %>%
    t()
rownames(sbs_mtx_neuro) <- colnames(neurons)

# Filter features
filt_low_genes2 <-
    colSums(sbs_mtx_neuro) %>%
    .[. > quantile(., 0.01)] %>%
    names()
sbs_mtx_neuro %<>% .[, filt_low_genes2]

min_filt_vector2 <-
    sbs_mtx_neuro %>%
    as_tibble() %>%
    select(all_of(filt_low_genes2)) %>%
    summarise(across(.fns = ~ quantile(.x, .005))) %>%
    as.list %>%
    map(as.double) %>%
    simplify %>%
    .[filt_low_genes2]

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


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

neurons$gaba_expr <-
  content_sbs_mtx_neuro %>%
  select(Gad1, Gad2, Slc32a1) %>%
  mutate(gaba = if_any(.fns = ~ .x > 0)) %>%
  .$gaba

neurons$glut_status <-
  content_sbs_mtx_neuro %>%
  select(Slc17a6) %>%
  mutate(glut = Slc17a6 > 0) %>%
  .$glut

neuro_fin <-
  subset(neurons,
    cells = union(
      WhichCells(neurons,
        expression = gaba_status == TRUE & glut_status == FALSE),
      WhichCells(neurons,
        expression = glut_status == TRUE & gaba_expr == FALSE)))

neuro_fin$status <- neuro_fin$gaba_status %>%
  if_else(true = "GABAergic",
    false = "glutamatergic")
Idents(neuro_fin) <- "status"

sbs_mtx_neuro <-
    neuro_fin %>%
    GetAssayData("data", "RNA") %>%
    as.data.frame() %>%
    t()
rownames(sbs_mtx_neuro) <- colnames(neuro_fin)

# Filter features
filt_low_genes2 <-
    colSums(sbs_mtx_neuro) %>%
    .[. > quantile(., 0.01)] %>%
    names()
sbs_mtx_neuro %<>% .[, filt_low_genes2]

min_filt_vector2 <-
    sbs_mtx_neuro %>%
    as_tibble() %>%
    select(all_of(filt_low_genes2)) %>%
    summarise(across(.fns = ~ quantile(.x, .005))) %>%
    as.list %>%
    map(as.double) %>%
    simplify %>%
    .[filt_low_genes2]

# Prepare table of intersection sets analysis
content_sbs_mtx_neuro <-
    (sbs_mtx_neuro > min_filt_vector2) %>%
    as_tibble() %>%
    mutate_all(as.numeric)
sbs_mtx_neuro_full <- content_sbs_mtx_neuro |> 
  select(any_of(c(neurotrans, glutr, gabar, "Gal", "Galr1"))) |> 
  dplyr::bind_cols(neuro_fin@meta.data)

sbs_mtx_neuro_full |> glimpse()
Rows: 164
Columns: 107
$ Slc17a6                       <dbl> 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Slc17a7                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Slc17a8                       <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0…
$ Slc1a1                        <dbl> 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Slc1a2                        <dbl> 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1…
$ Slc1a6                        <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
$ Gad1                          <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Slc32a1                       <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Slc6a1                        <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1…
$ Gria1                         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1…
$ Gria2                         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Gria3                         <dbl> 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
$ Gria4                         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1…
$ Grid1                         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1…
$ Grid2                         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Grik1                         <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Grik2                         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Grik3                         <dbl> 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0…
$ Grik4                         <dbl> 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1…
$ Grik5                         <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1…
$ Grin1                         <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1…
$ Grin2a                        <dbl> 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0…
$ Grin2b                        <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Grin2c                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Grin2d                        <dbl> 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1…
$ Grin3a                        <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1…
$ Grin3b                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Grm1                          <dbl> 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1…
$ Grm5                          <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Grm2                          <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
$ Grm3                          <dbl> 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1…
$ Grm4                          <dbl> 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1…
$ Grm7                          <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1…
$ Grm8                          <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1…
$ Gabra1                        <dbl> 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1…
$ Gabra2                        <dbl> 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1…
$ Gabra3                        <dbl> 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1…
$ Gabra4                        <dbl> 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1…
$ Gabra5                        <dbl> 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1…
$ Gabrb1                        <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1…
$ Gabrb2                        <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1…
$ Gabrb3                        <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Gabrg1                        <dbl> 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1…
$ Gabrg2                        <dbl> 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1…
$ Gabrg3                        <dbl> 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Gabrd                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Gabrq                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Gabrr2                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Gabbr1                        <dbl> 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1…
$ Gabbr2                        <dbl> 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1…
$ Galr1                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ nCount_RAW                    <dbl> 6712, 5209, 18763, 6898, 3283, 15041, 10…
$ nFeature_RAW                  <int> 3046, 2466, 5240, 2954, 1840, 4537, 3708…
$ nCount_SCT                    <dbl> 6663, 5947, 6715, 6787, 5980, 7136, 7433…
$ nFeature_SCT                  <int> 3027, 2443, 2900, 2936, 1830, 3251, 3624…
$ nCount_RNA                    <dbl> 6690, 5196, 18708, 6876, 3277, 15002, 10…
$ nFeature_RNA                  <int> 3046, 2465, 5240, 2954, 1840, 4532, 3705…
$ log10GenesPerUMI              <dbl> 0.9106776, 0.9128415, 0.8706244, 0.90438…
$ percent_mito                  <dbl> 0.014947683, 0.000000000, 0.010690614, 0…
$ percent_ribo                  <dbl> 0.17937220, 0.15396459, 0.17104982, 0.29…
$ percent_hb                    <dbl> 0.014947683, 0.000000000, 0.010690614, 0…
$ var_regex                     <dbl> 2.152466, 4.792148, 2.902502, 4.479348, …
$ S.Score                       <dbl> -0.0460504870, 0.0340772974, -0.02898078…
$ G2M.Score                     <dbl> -0.0505531289, -0.0806596774, -0.0113846…
$ log_prob_doublet              <dbl> -47.18951, -298.95695, -378.18579, -293.…
$ orig.ident                    <chr> "Pr5P7", "Pr5P7", "Pr5P7", "Pr5P7", "Pr5…
$ comb_clstr1                   <fct> 1, 1, 5, 1, 9, 3, 3, 2, 7, 7, 1, 1, 3, 5…
$ comb_clstr2                   <fct> 5, 3, 6, 3, 10, 1, 1, 1, 5, 5, 5, 5, 1, …
$ QC                            <chr> "Pass", "Pass", "Pass", "Pass", "Pass", …
$ cell_name                     <chr> "Pr5P7_CTACAGACACCAGTTA-1", "Pr5P7_GGTGT…
$ RNA_snn_res.0.2               <fct> 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2…
$ RNA_snn_res.0.228090737068544 <fct> 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2…
$ RNA_snn_res.0.257501399916775 <fct> 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2…
$ RNA_snn_res.0.288327257678505 <fct> 2, 2, 1, 2, 1, 3, 3, 3, 2, 2, 2, 2, 3, 3…
$ RNA_snn_res.0.320672973912272 <fct> 2, 2, 1, 2, 1, 3, 3, 3, 2, 2, 2, 2, 3, 3…
$ RNA_snn_res.0.354653793841904 <fct> 2, 2, 1, 2, 1, 3, 3, 3, 2, 2, 2, 2, 3, 3…
$ RNA_snn_res.0.390396916313969 <fct> 1, 1, 2, 1, 2, 3, 3, 3, 1, 1, 1, 1, 3, 3…
$ RNA_snn_res.0.42804308489375  <fct> 1, 1, 2, 1, 2, 3, 3, 3, 1, 1, 1, 1, 3, 3…
$ RNA_snn_res.0.467748440052595 <fct> 1, 1, 2, 1, 2, 3, 3, 3, 1, 1, 1, 1, 3, 3…
$ RNA_snn_res.0.509686683836658 <fct> 1, 1, 2, 1, 2, 3, 3, 3, 1, 1, 1, 1, 3, 3…
$ RNA_snn_res.0.55405162030143  <fct> 1, 1, 2, 1, 2, 3, 3, 3, 1, 1, 1, 1, 3, 3…
$ RNA_snn_res.0.601060150080292 <fct> 1, 1, 2, 1, 2, 3, 3, 3, 1, 1, 1, 1, 3, 3…
$ RNA_snn_res.0.650955816707991 <fct> 1, 1, 3, 1, 5, 2, 2, 2, 1, 1, 1, 1, 2, 2…
$ RNA_snn_res.0.704013027062187 <fct> 1, 1, 3, 1, 5, 2, 2, 2, 1, 1, 1, 1, 2, 2…
$ RNA_snn_res.0.760542100314118 <fct> 1, 1, 3, 1, 5, 2, 2, 2, 1, 1, 1, 1, 2, 2…
$ RNA_snn_res.0.820895341557435 <fct> 1, 1, 3, 1, 5, 2, 2, 2, 1, 1, 1, 1, 2, 2…
$ RNA_snn_res.0.885474391225604 <fct> 1, 1, 3, 1, 5, 2, 2, 2, 1, 1, 1, 1, 2, 2…
$ RNA_snn_res.0.954739174288687 <fct> 1, 1, 3, 1, 5, 2, 2, 2, 2, 1, 1, 1, 2, 2…
$ RNA_snn_res.1.02921887079277  <fct> 2, 2, 3, 2, 6, 1, 1, 1, 4, 4, 4, 4, 1, 1…
$ RNA_snn_res.1.10952546122871  <fct> 2, 2, 3, 2, 6, 1, 1, 1, 4, 4, 4, 4, 1, 1…
$ RNA_snn_res.1.19637058047566  <fct> 2, 2, 3, 2, 6, 1, 1, 1, 4, 4, 4, 4, 1, 1…
$ RNA_snn_res.1.29058666317047  <fct> 2, 2, 4, 2, 6, 1, 1, 1, 3, 3, 3, 3, 1, 1…
$ RNA_snn_res.1.3931605212185   <fct> 2, 2, 4, 2, 6, 1, 1, 1, 3, 3, 3, 3, 1, 1…
$ RNA_snn_res.1.50529998950619  <fct> 2, 2, 4, 2, 6, 1, 1, 1, 3, 3, 3, 3, 1, 4…
$ RNA_snn_res.1.62850667240407  <fct> 1, 1, 4, 1, 6, 2, 2, 7, 3, 3, 3, 3, 7, 2…
$ RNA_snn_res.1.76469908534095  <fct> 1, 1, 4, 1, 6, 4, 2, 7, 3, 3, 3, 3, 7, 2…
$ RNA_snn_res.1.91638172569127  <fct> 3, 1, 4, 1, 6, 4, 2, 7, 3, 3, 3, 3, 7, 2…
$ RNA_snn_res.2.08652576169017  <fct> 3, 2, 5, 2, 6, 11, 1, 7, 3, 3, 3, 3, 7, …
$ RNA_snn_res.2.27924774611412  <fct> 9, 2, 4, 2, 7, 4, 1, 6, 5, 5, 5, 5, 6, 4…
$ RNA_snn_res.2.50001           <fct> 10, 2, 4, 2, 6, 4, 1, 5, 7, 7, 7, 7, 5, …
$ seurat_clusters               <fct> 10, 2, 4, 2, 6, 4, 1, 5, 7, 7, 7, 7, 5, …
$ k_tree                        <fct> 2, 2, 3, 2, 6, 1, 1, 1, 4, 4, 4, 4, 1, 1…
$ sep_clstr                     <fct> 2, 2, 3, 2, 6, 1, 1, 1, 4, 4, 4, 4, 1, 1…
$ gaba_status                   <lgl> TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, F…
$ gaba_expr                     <lgl> TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, F…
$ glut_status                   <lgl> FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, T…
$ status                        <chr> "GABAergic", "GABAergic", "GABAergic", "…
sbs_mtx_neuro_full$Gal_sign <- 
  sbs_mtx_neuro_full %>%
  select(Galr1) %>%
  mutate(Gal_sign = if_any(.fns = ~ .x > 0)) %>%
  .$Gal_sign

# for reproducibility
set.seed(reseed)
# plot
ggpiestats(
  # arguments relevant for `ggpiestats()`
  data = sbs_mtx_neuro_full |> filter(status == "glutamatergic"),
  x = Gal_sign,
  perc.k = 1,
  package = "ggsci",
  palette = "category10_d3",
  # arguments relevant for `combine_plots()`
  title.text = "Molecular specification of Pr5 neuronal lineages by Galr1 signalling and main glutamatergic neurotransmitter expression",
  caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
  plotgrid.args = list(nrow = 1)
)

Molecular specification of Pr5 neuronal lineages by Galr1 signalling and main glutamatergic neurotransmitter expression (no GABAergic cells express Galr1)

p1 <- FeaturePlot_scCustom(
  srt, "Slc17a6",
  pt.size = 2,
  order = TRUE,
  alpha_exp = 0.65,
  alpha_na_exp = 0.2,
  label = TRUE,
  repel = TRUE,
  colors_use = srt@misc$expr_Colour_Pal) + 
  ggtitle("Slc17a6(Vglut2): ") + theme(plot.title = element_text(size = 24))
p1

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
Plot_Density_Custom(
  seurat_object = srt,
  features = "Slc17a6",
  pt.size = 2) + 
  ggtitle("Slc17a6(Vglut2): ") + theme(plot.title = element_text(size = 24))

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
Plot_Density_Custom(
  seurat_object = srt,
  features = "Galr1",
  pt.size = 2) + 
  ggtitle("Galr1: ") + theme(plot.title = element_text(size = 24))

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
Plot_Density_Joint_Only(
  seurat_object = srt,
  features = c("Slc17a6", "Galr1"),
  pt.size = 2) + 
  ggtitle("Slc17a6(Vglut2) + Galr1 ") + theme(plot.title = element_text(size = 24))

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
srt_vglut2 <- subset(srt, idents = c(1, 2, 3, 4, 5, 6, 9))
srt_vglut2 <- subset(srt_vglut2, subset = UMAP_1 > -6 & UMAP_1 < 9)
p2 <- FeaturePlot_scCustom(
  srt_vglut2, "Galr1",
  pt.size = 5,
  order = TRUE,
  alpha_exp = 0.75,
  alpha_na_exp = 0.05,
  colors_use = srt@misc$expr_Colour_Pal) + 
  ggtitle("Galr1 in Vglut2+ populations") + theme(plot.title = element_text(size = 24))
p2

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
Plot_Density_Custom(
  seurat_object = srt_vglut2,
  features = "Galr1",
  pt.size = 2) + 
  ggtitle("Galr1 across Vglut cell-populations only") + theme(plot.title = element_text(size = 24))

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
Plot_Density_Joint_Only(
  seurat_object = srt_vglut2,
  features = c("Slc17a6", "Galr1"),
  pt.size = 2) + 
  ggtitle("Slc17a6 + Galr1 across Vglut cell-populations only") + theme(plot.title = element_text(size = 24))

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
DimPlot_scCustom(
  srt_vglut2,
  label = TRUE,
  repel = TRUE,
  pt.size = 3,
  figure_plot = TRUE
  ) + ggtitle("Vglut2+ clusters") + NoLegend()

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
DoHeatmap(srt_vglut2, features = genes_present$found_features)

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
p3 <- DotPlot_scCustom(
  seurat_object = srt_vglut2,
  assay = "RNA",
  features = genes_present$found_features,
  flip_axes = TRUE,
  x_lab_rotate = TRUE,
  dot.min = .05,
  dot.scale = 10,
  colors_use = viridis(n = 30, alpha = .75, direction = -1, option = "E"))
p3

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 
p1 | p2 | p3

Version Author Date
4ef209f Evgenii O. Tretiakov 2023-03-05 

sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] gprofiler2_0.2.1      mrtree_0.0.0.9000     Nebulosa_1.8.0       
 [4] scCustomize_1.1.1     Scillus_0.5.0         qs_0.25.4            
 [7] ggstatsplot_0.10.0    UpSetR_1.4.0          patchwork_1.1.2.9000 
[10] glmGamPoi_1.10.2      sctransform_0.3.5     SeuratDisk_0.0.0.9020
[13] SeuratWrappers_0.3.1  SeuratObject_4.1.3    Seurat_4.3.0         
[16] reticulate_1.28       kableExtra_1.3.4      zeallot_0.1.0        
[19] future_1.31.0         skimr_2.1.5           magrittr_2.0.3       
[22] lubridate_1.9.0       timechange_0.1.1      forcats_0.5.2        
[25] stringr_1.5.0         dplyr_1.1.0           purrr_1.0.1          
[28] readr_2.1.3           tidyr_1.3.0           tibble_3.1.8         
[31] ggplot2_3.4.1         tidyverse_1.3.2.9000  viridis_0.6.2        
[34] viridisLite_0.4.1     RColorBrewer_1.1-3    here_1.0.1           
[37] workflowr_1.7.0      

loaded via a namespace (and not attached):
  [1] rsvd_1.0.5                  ica_1.0-3                  
  [3] svglite_2.1.0               ps_1.7.2                   
  [5] foreach_1.5.2               lmtest_0.9-40              
  [7] rprojroot_2.0.3             crayon_1.5.2               
  [9] MASS_7.3-58.1               nlme_3.1-161               
 [11] backports_1.4.1             rlang_1.0.6                
 [13] XVector_0.38.0              ROCR_1.0-11                
 [15] performance_0.10.2.4        irlba_2.3.5.1              
 [17] callr_3.7.3                 stringfish_0.15.7          
 [19] data.tree_1.0.0             rjson_0.2.21               
 [21] bit64_4.0.5                 glue_1.6.2                 
 [23] parallel_4.2.2              processx_3.8.0             
 [25] vipor_0.4.5                 spatstat.sparse_3.0-0      
 [27] BiocGenerics_0.44.0         spatstat.geom_3.0-6        
 [29] tidyselect_1.2.0            SummarizedExperiment_1.28.0
 [31] fitdistrplus_1.1-8          zoo_1.8-11                 
 [33] MatrixModels_0.5-1          xtable_1.8-4               
 [35] formattable_0.2.1           evaluate_0.20              
 [37] cli_3.6.0                   zlibbioc_1.44.0            
 [39] rstudioapi_0.14             miniUI_0.1.1.1             
 [41] sp_1.6-0                    whisker_0.4.1              
 [43] bslib_0.4.2                 fastmatch_1.1-3            
 [45] treeio_1.23.0               maps_3.4.1                 
 [47] shiny_1.7.4                 xfun_0.37                  
 [49] clue_0.3-63                 parameters_0.20.2          
 [51] cluster_2.1.4               tidygraph_1.2.2            
 [53] clusterGeneration_1.3.7     expm_0.999-6               
 [55] SymSim_0.0.0.9000           ggrepel_0.9.2.9999         
 [57] ape_5.6-2                   dendextend_1.16.0          
 [59] listenv_0.9.0               png_0.1-8                  
 [61] withr_2.5.0                 ggforce_0.4.1.9000         
 [63] bitops_1.0-7                plyr_1.8.8                 
 [65] pracma_2.4.2                coda_0.19-4                
 [67] pillar_1.8.1                RcppParallel_5.1.5         
 [69] GlobalOptions_0.1.2         cachem_1.0.6               
 [71] multcomp_1.4-20             fs_1.6.1                   
 [73] scatterplot3d_0.3-42        hdf5r_1.3.7                
 [75] GetoptLong_1.0.5            paletteer_1.5.0            
 [77] vctrs_0.5.2                 ellipsis_0.3.2             
 [79] generics_0.1.3              RApiSerialize_0.1.2        
 [81] tools_4.2.2                 beeswarm_0.4.0             
 [83] tweenr_2.0.2                munsell_0.5.0              
 [85] emmeans_1.8.3               DelayedArray_0.24.0        
 [87] fastmap_1.1.0               compiler_4.2.2             
 [89] abind_1.4-5                 httpuv_1.6.9               
 [91] ggimage_0.3.1               plotly_4.10.1              
 [93] GenomeInfoDbData_1.2.9      gridExtra_2.3              
 [95] lattice_0.20-45             deldir_1.0-6               
 [97] utf8_1.2.3                  later_1.3.0                
 [99] prismatic_1.1.1             jsonlite_1.8.4             
[101] scales_1.2.1                tidytree_0.4.2             
[103] pbapply_1.7-0               estimability_1.4.1         
[105] lazyeval_0.2.2              promises_1.2.0.1           
[107] doParallel_1.0.17           R.utils_2.12.2             
[109] goftest_1.2-3               effectsize_0.8.3.2         
[111] spatstat.utils_3.0-1        checkmate_2.1.0            
[113] rmarkdown_2.20              sandwich_3.0-2             
[115] cowplot_1.1.1               webshot_0.5.4              
[117] Rtsne_0.16                  Biobase_2.58.0             
[119] uwot_0.1.14                 igraph_1.3.5               
[121] survival_3.4-0              numDeriv_2016.8-1.1        
[123] yaml_2.3.7                  plotrix_3.8-2              
[125] systemfonts_1.0.4           htmltools_0.5.4            
[127] graphlayouts_0.8.4          IRanges_2.32.0             
[129] quadprog_1.5-8              digest_0.6.31              
[131] mime_0.12                   repr_1.1.4                 
[133] bayestestR_0.13.0           yulab.utils_0.0.6          
[135] future.apply_1.10.0         ggmin_0.0.0.9000           
[137] remotes_2.4.2               data.table_1.14.8          
[139] S4Vectors_0.36.1            R.oo_1.25.0                
[141] labeling_0.4.2              splines_4.2.2              
[143] rematch2_2.1.2              RCurl_1.98-1.9             
[145] ks_1.14.0                   hms_1.1.2                  
[147] colorspace_2.1-0            base64enc_0.1-3            
[149] BiocManager_1.30.19         mnormt_2.1.1               
[151] ggbeeswarm_0.7.1.9000       GenomicRanges_1.50.2       
[153] shape_1.4.6                 aplot_0.1.9                
[155] ggrastr_1.0.1               sass_0.4.5                 
[157] Rcpp_1.0.10                 mclust_6.0.0               
[159] RANN_2.6.1                  mvtnorm_1.1-3              
[161] circlize_0.4.15             fansi_1.0.4                
[163] tzdb_0.3.0                  parallelly_1.34.0          
[165] R6_2.5.1                    grid_4.2.2                 
[167] ggridges_0.5.4              lifecycle_1.0.3            
[169] phytools_1.2-0              statsExpressions_1.3.6     
[171] BayesFactor_0.9.12-4.4      datawizard_0.6.5           
[173] leiden_0.4.3                phangorn_2.10.0            
[175] jquerylib_0.1.4             snakecase_0.11.0           
[177] Matrix_1.5-3                RcppAnnoy_0.0.20           
[179] TH.data_1.1-1               iterators_1.0.14           
[181] spatstat.explore_3.0-6      htmlwidgets_1.6.1          
[183] polyclip_1.10-4             gridGraphics_0.5-1         
[185] optimParallel_1.0-2         rvest_1.0.3                
[187] ComplexHeatmap_2.14.0       globals_0.16.2             
[189] insight_0.19.0              spatstat.random_3.1-3      
[191] progressr_0.13.0            codetools_0.2-18           
[193] matrixStats_0.63.0          getPass_0.2-2              
[195] SingleCellExperiment_1.20.0 RSpectra_0.16-1            
[197] R.methodsS3_1.8.2           GenomeInfoDb_1.34.9        
[199] correlation_0.8.3           gtable_0.3.1               
[201] git2r_0.30.1                stats4_4.2.2               
[203] highr_0.10                  ggfun_0.0.9                
[205] tensor_1.5                  httr_1.4.4                 
[207] KernSmooth_2.23-20          stringi_1.7.12             
[209] farver_2.1.1                reshape2_1.4.4             
[211] ggtree_3.7.1.002            magick_2.7.3               
[213] xml2_1.3.3                  combinat_0.0-8             
[215] ggplotify_0.1.0             scattermore_0.8            
[217] bit_4.0.5                   clustree_0.5.0             
[219] MatrixGenerics_1.10.0       spatstat.data_3.0-0        
[221] ggraph_2.1.0.9000           janitor_2.2.0.9000         
[223] pkgconfig_2.0.3             ggprism_1.0.4              
[225] knitr_1.42