Exploratory analysis of CaCyBP and S100a6 expression in embryonic mice cortex dataset with focus on astrocytic lineage

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Last updated: 2024-06-09

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Knit directory: Cinquina_2024/

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File	Version	Author	Date	Message
Rmd	62900cf	Evgenii O. Tretiakov	2024-06-09	add header to fix vector output in pdf
Rmd	fd0e9b9	Evgenii O. Tretiakov	2024-06-09	add visualisation of cortical data analysis of CaCyBP and S100a6
html	fd0e9b9	Evgenii O. Tretiakov	2024-06-09	add visualisation of cortical data analysis of CaCyBP and S100a6

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# Create a vector with the stage of development for each object
stage_info <- c("E11.5", "E12.5", "E13.5", "E14.5", "E15.5", "E16", "E18.5", "E18", "P1", "P1", "E10", "E17.5", "P4")

merged_cortex_2 <- SeuratObject::LoadSeuratRds(here::here("data/azimuth_integrated.rds"))
merged_cortex_2$cell_name <- Cells(merged_cortex_2)
merged_cortex_2

An object of class Seurat 
28186 features across 82415 samples within 5 assays 
Active assay: RNA (27998 features, 2000 variable features)
 25 layers present: data.E11.5, data.E12.5, data.E13.5, data.E14.5, data.E15.5, data.E16, data.E18.5, data.E18, data.P1, data.E10, data.E17.5, data.P4, scale.data, counts.E11.5, counts.E12.5, counts.E13.5, counts.E14.5, counts.E15.5, counts.E16, counts.E18.5, counts.E18, counts.P1, counts.E10, counts.E17.5, counts.P4
 4 other assays present: prediction.score.class, prediction.score.cluster, prediction.score.subclass, prediction.score.cross_species_cluster
 7 dimensional reductions calculated: pca, integrated_dr, ref.umap, integrated.cca, umap.cca, harmony, umap.harmony

orig_umap <- readr::read_tsv(
  here("data/SCP1290/cluster/cluster_scDevSC.merged.umap.txt"),
  skip = 2,
  col_names = c("cell_name", "UMAP_1", "UMAP_2"),
  col_types = list(col_character(), col_double(), col_double())
)

glimpse(orig_umap)

Rows: 98,047
Columns: 3
$ cell_name <chr> "E10_v1_AAACCTGAGGGTCTCC-1", "E10_v1_AAACCTGCACAACGCC-1", "E…
$ UMAP_1    <dbl> -3.0025911, -3.6729214, -3.8859395, -3.9020242, -2.9312939, …
$ UMAP_2    <dbl> -10.453364, -6.552985, -10.773631, -10.869657, -10.769403, -…

orig_umap %<>% tibble::column_to_rownames("cell_name")
orig_umap %<>% as.matrix()
orig_tsne <- readr::read_tsv(
  here("data/SCP1290/cluster/cluster_scDevSC.merged.tsne.txt"),
  skip = 2,
  col_names = c("cell_name", "tSNE_1", "tSNE_2"),
  col_types = list(col_character(), col_double(), col_double())
)
glimpse(orig_tsne)

Rows: 98,047
Columns: 3
$ cell_name <chr> "E10_v1_AAACCTGAGGGTCTCC-1", "E10_v1_AAACCTGCACAACGCC-1", "E…
$ tSNE_1    <dbl> 15.442958, 10.373660, 14.828413, 16.307658, 18.062250, 13.72…
$ tSNE_2    <dbl> -19.603245, -17.062466, -20.102599, -20.003542, -18.636268, …

orig_tsne %<>% tibble::column_to_rownames("cell_name")
orig_tsne %<>% as.matrix()
orig_metadata <- readr::read_tsv(here(
  "data/SCP1290/metadata/metaData_scDevSC.txt"))
orig_metadata %<>% rename("cell_name" = "NAME")
orig_metadata_types <- orig_metadata[1,] |> purrr::simplify()
orig_metadata %<>% filter(!cell_name == "TYPE")
glimpse(orig_metadata)

Rows: 98,047
Columns: 28
$ cell_name                                    <chr> "E10_v1_AAACCTGAGGGTCTCC-…
$ orig_ident                                   <chr> "E10", "E10", "E10", "E10…
$ nCount_RNA                                   <chr> "1544", "1157", "2081", "…
$ nFeature_RNA                                 <chr> "1022", "783", "1200", "1…
$ percent_mito                                 <chr> "0.02007772", "0.01469317…
$ n_hkgene                                     <chr> "51", "39", "67", "71", "…
$ S_Score                                      <chr> "0.356987282", "0.4538538…
$ G2M_Score                                    <chr> "0.330795055", "0.2605599…
$ Phase                                        <chr> "S", "S", "S", "G2M", "S"…
$ CC_Difference                                <chr> "0.026192226", "0.1932938…
$ seurat_clusters                              <chr> "34", "34", "34", "37", "…
$ RNA_snn_res_1                                <chr> "20", "20", "20", "20", "…
$ scrublet_doublet                             <chr> "FALSE", "FALSE", "FALSE"…
$ RNA_snn_res_2                                <chr> "34", "34", "34", "37", "…
$ Doublet_intersect                            <chr> NA, NA, NA, NA, NA, NA, N…
$ Gral_cellType                                <chr> NA, NA, NA, NA, NA, NA, N…
$ New_cellType                                 <chr> "Apical progenitors", "In…
$ biosample_id                                 <chr> "E10", "E10", "E10", "E10…
$ donor_id                                     <chr> "mouse_E10", "mouse_E10",…
$ species                                      <chr> "NCBITaxon_10090", "NCBIT…
$ disease                                      <chr> "PATO_0000461", "PATO_000…
$ disease__ontology_label                      <chr> "normal", "normal", "norm…
$ organ                                        <chr> "UBERON_0008930", "UBERON…
$ organ__ontology_label                        <chr> "somatosensory cortex", "…
$ library_preparation_protocol                 <chr> "EFO_0009899", "EFO_00098…
$ library_preparation_protocol__ontology_label <chr> "10X 3' v2 sequencing", "…
$ sex                                          <chr> "mixed", "mixed", "mixed"…
$ species__ontology_label                      <chr> "Mus musculus", "Mus musc…

change_column_types <- function(df, types) {
  for (col_name in names(types)) {
    col_type <- types[col_name]
    
    if (col_type == "character") {
      df[[col_name]] <- as.character(df[[col_name]])
    } else if (col_type == "numeric") {
      df[[col_name]] <- as.numeric(df[[col_name]])
    } else if (col_type == "integer") {
      df[[col_name]] <- as.integer(df[[col_name]])
    } else if (col_type == "logical") {
      df[[col_name]] <- as.logical(df[[col_name]])
    } else if (col_type == "factor") {
      df[[col_name]] <- as.factor(df[[col_name]])
    } else if (col_type == "group") {
      df[[col_name]] <- as.factor(df[[col_name]])
    } else {
      warning(paste("Unknown type:", col_type, "for column", col_name))
    }
  }
  
  return(df)
}

# Apply the function to the metadata
orig_metadata <- change_column_types(orig_metadata, orig_metadata_types)

# Print the modified metadata
glimpse(orig_metadata)

Rows: 98,047
Columns: 28
$ cell_name                                    <chr> "E10_v1_AAACCTGAGGGTCTCC-…
$ orig_ident                                   <fct> E10, E10, E10, E10, E10, …
$ nCount_RNA                                   <dbl> 1544, 1157, 2081, 2490, 2…
$ nFeature_RNA                                 <dbl> 1022, 783, 1200, 1430, 14…
$ percent_mito                                 <dbl> 0.020077720, 0.014693172,…
$ n_hkgene                                     <dbl> 51, 39, 67, 71, 70, 50, 4…
$ S_Score                                      <dbl> 0.35698728, 0.45385381, 0…
$ G2M_Score                                    <dbl> 0.33079506, 0.26055995, 0…
$ Phase                                        <fct> S, S, S, G2M, S, S, S, S,…
$ CC_Difference                                <dbl> 0.026192226, 0.193293862,…
$ seurat_clusters                              <fct> 34, 34, 34, 37, 37, 34, 4…
$ RNA_snn_res_1                                <fct> 20, 20, 20, 20, 20, 20, 3…
$ scrublet_doublet                             <fct> FALSE, FALSE, FALSE, FALS…
$ RNA_snn_res_2                                <fct> 34, 34, 34, 37, 37, 34, 4…
$ Doublet_intersect                            <fct> NA, NA, NA, NA, NA, NA, N…
$ Gral_cellType                                <fct> NA, NA, NA, NA, NA, NA, N…
$ New_cellType                                 <fct> Apical progenitors, Inter…
$ biosample_id                                 <fct> E10, E10, E10, E10, E10, …
$ donor_id                                     <fct> mouse_E10, mouse_E10, mou…
$ species                                      <fct> NCBITaxon_10090, NCBITaxo…
$ disease                                      <fct> PATO_0000461, PATO_000046…
$ disease__ontology_label                      <fct> normal, normal, normal, n…
$ organ                                        <fct> UBERON_0008930, UBERON_00…
$ organ__ontology_label                        <fct> somatosensory cortex, som…
$ library_preparation_protocol                 <fct> EFO_0009899, EFO_0009899,…
$ library_preparation_protocol__ontology_label <fct> 10X 3' v2 sequencing, 10X…
$ sex                                          <fct> mixed, mixed, mixed, mixe…
$ species__ontology_label                      <fct> Mus musculus, Mus musculu…

orig_srt <- Read10X(data.dir = here("data/SCP1290/expression/601ae2f4771a5b0d72588bfb"))

# Convert the log1p normalized matrix to a standard matrix if it's not already
normalized_matrix <- as.matrix(orig_srt)

# Reverse the log1p transformation to get the count matrix
count_matrix <- expm1(normalized_matrix)

# Convert the count matrix to a sparse matrix format (dgCMatrix) if needed
count_matrix_sparse <- as(count_matrix, "dgCMatrix")

# Create a Seurat object using the recovered count matrix
merged_cortex <- CreateSeuratObject(counts = count_matrix_sparse, meta.data = orig_metadata)

merged_cortex[["umap"]] <- CreateDimReducObject(embeddings = orig_umap, key = "UMAP_", assay = DefaultAssay(merged_cortex))
merged_cortex[["tsne"]] <- CreateDimReducObject(embeddings = orig_tsne, key = "tSNE_", assay = DefaultAssay(merged_cortex))

merged_cortex$stage <- merged_cortex$orig.ident
table(merged_cortex$New_cellType)

      Apical progenitors               Astrocytes      Cajal Retzius cells 
                   18491                     2976                      532 
                   CThPN      Cycling glial cells                   DL CPN 
                    4607                     1004                     3106 
                DL_CPN_1                 DL_CPN_2                  Doublet 
                     422                      146                     1854 
       Endothelial cells            Ependymocytes         Immature neurons 
                     291                       35                     3092 
Intermediate progenitors             Interneurons                  Layer 4 
                    8490                    10469                     5317 
                Layer 6b        Low quality cells                Microglia 
                     194                     4545                      263 
       Migrating neurons                       NP         Oligodendrocytes 
                   12332                      424                     1098 
               Pericytes          Red blood cells                     SCPN 
                     236                      330                     2987 
                  UL CPN                     VLMC 
                   14041                      765 

Idents(merged_cortex) <- "New_cellType"
merged_cortex <- subset(merged_cortex, idents = c("Doublet", "Low quality cells", "Red blood cells"), invert = TRUE)

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

# Get the list of S100 family genes
s100_genes <- grep("^S100", rownames(merged_cortex), value = TRUE)

genes.embed <- c(
  "Abcd1",
  "Abcd2",
  "Abcd3",
  "Acaa1",
  "Acaa2",
  "Acox1",
  "Agrn",
  "Agt",
  "Alcam",
  "Aldh1a1",
  "Aldh1l1",
  "Aldoc",
  "Angpt1",
  "Apoe",
  "App",
  "Aqp4",
  "Arf1",
  "Bmp7",
  "Bsg",
  "Cacybp",
  "Caf4",
  "Ccl25",
  "Ckb",
  "Cnr1",
  "Cnr2",
  "Col4a5",
  "Cst3",
  "Dagla",
  "Daglb",
  "Decr2",
  "Dcc",
  "Dnm1",
  "Drp1",
  "Ech1",
  "Efna5",
  "Egfr",
  "Enho",
  "Eno1",
  "Faah",
  "Fgf1",
  "Fgfr3",
  "Fis1",
  "Fos",
  "Fth1",
  "Ftl1",
  "Gfap",
  "Gja1",
  "Gli1",
  "Glul",
  "Gnai2",
  "Gnas",
  "H2-K1",
  "Hacd2",
  "Hadhb",
  "Hbegf",
  "Hepacam",
  "Hif1",
  "Htra1",
  "Igsf1",
  "Il18",
  "Il1rapl1",
  "Itgav",
  "Jam2",
  "Lama2",
  "Lamb2",
  "Lcat",
  "Lgi1",
  "Lgi4",
  "Lpcat3",
  "Lrpap1",
  "Lrrc4b",
  "Lxn",
  "Mdk",
  "Mdv1",
  "Mfn1",
  "Mfn2",
  "Mgll",
  "Mief1",
  "Napepld",
  "Ncam1",
  "Ncan",
  "Ndrg2",
  "Nfasc",
  "Nfia",
  "Nlgn3",
  "Nrxn1",
  "Nrxn2",
  "Ntn1",
  "Ntrk3",
  "Opa1",
  "Otp",
  "Pex1",
  "Pex10",
  "Pex12",
  "Pex13",
  "Pex14",
  "Pex16",
  "Pex2",
  "Pex26",
  "Pex3",
  "Pex6",
  "Pkm",
  "Pla2g7",
  "Plcb1",
  "Psap",
  "Ptn",
  "Pygb",
  "Ralyl",
  "Rgma",
  "Rtn4",
  "S100a1",
  "S100a6",
  "S100b",
  "Siah1a",
  "Siah1b",
  "Scd2",
  "Sdc2",
  "Sema6a",
  "Sema6d",
  "Sgcd",
  "Sirpa",
  "Slc1a2",
  "Slc1a3",
  "Slc38a1",
  "Slc4a4",
  "Slc6a11",
  "Slc7a10",
  "Slit1",
  "Slit2",
  "Slitrk2",
  "Sorbs1",
  "Sox9",
  "Sparc",
  "Spon1",
  "Tafa1",
  "Timp3",
  "Tkt",
  "Trpv1",
  "Vcam1",
  "Vegfa"
) %>% .[. %in% rownames(merged_cortex)]

merged_cortex <- FindVariableFeatures(merged_cortex, nfeatures = 5000, verbose = FALSE)
merged_cortex <- NormalizeData(
  merged_cortex,
  features = c(
    VariableFeatures(merged_cortex),
    s100_genes,
    genes.embed),
  verbose = FALSE)
# Scale data
merged_cortex <- ScaleData(
  merged_cortex,
  features = c(
    VariableFeatures(merged_cortex),
    s100_genes,
    genes.embed),
  verbose = FALSE)

# Create DimPlot
p1 <- DimPlot(
  merged_cortex,
  reduction = "umap",
  group.by = c("stage", "New_cellType"),
  combine = FALSE, label.size = 2
)

p2 <- DimPlot(
  merged_cortex,
  reduction = "tsne",
  group.by = c("stage", "New_cellType"),
  combine = FALSE, label.size = 2
)

wrap_plots(c(p1, p2), ncol = 2, byrow = F)

Past versions of plot-stages-and-types-all-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

# Create a custom FeaturePlot for each S100 gene
plot_list <-
    lapply(
        c(s100_genes, "Cacybp", "Siah1a", "Siah1b"),
        function(gene) {
            FeaturePlot_scCustom(
                seurat_object = merged_cortex,
                features = gene,
                colors_use = merged_cortex@misc$expr_Colour_Pal,
                na_color = "lightgray",
                layer = "data",
                order = TRUE,
                pt.size = 1,
                reduction = "umap",
                split.by = "stage",
                split_collect = FALSE,
                label = F,
                label_feature_yaxis = TRUE,
                combine = FALSE
            )
        })


# Combine the plots into a single grid
combined_plot <- patchwork::wrap_plots(plot_list, ncol = 1)

# Display the combined plot
print(combined_plot)

Past versions of plot-s100-features-all-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

# Create a compact DotPlot
compact_plot <- DotPlot(
  object = merged_cortex,
  features = c(s100_genes,
               "Cacybp",
               "Siah1a",
               "Siah1b"),
  group.by = "stage",
  cluster.idents = FALSE,
  scale = TRUE,
  dot.scale = 12
) + RotatedAxis()

# Display the compact plot
print(compact_plot)

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Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

plot_gene_by_dev <- function(x) {
  f_plot <- FeaturePlot_scCustom(
    merged_cortex,
    colors_use = merged_cortex@misc$expr_Colour_Pal,
    features = x,
    layer = "data",
    max.cutoff = "q99",
    na_color = "lightgray",
    figure_plot = T,
    pt.size = 1,
    reduction = "umap",
    split.by = "stage",
    split_collect = FALSE,
    label = F,
    label_feature_yaxis = TRUE,
    combine = FALSE
  )
  
  print(f_plot)
}

genes.embed |> purrr::walk(plot_gene_by_dev)

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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fd0e9b9	Evgenii O. Tretiakov	2024-06-09

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Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

astro <- subset(
  x = merged_cortex,
  subset = New_cellType == c("Apical progenitors",
                             "Cycling glial cells",
                             "Astrocytes"))

astro <- FindVariableFeatures(astro, nfeatures = 5000, verbose = FALSE)

# Scale data
astro <- ScaleData(
  astro,
  features = c(
    VariableFeatures(astro),
    s100_genes,
    genes.embed),
  verbose = FALSE)

# Run PCA
astro <- RunPCA(astro, verbose = FALSE)

# Find neighbors
astro <- FindNeighbors(astro, reduction = "pca", dims = 1:30)

# Find clusters
astro <- FindClusters(astro, resolution = 0.7, cluster.name = "astro_clusters", algorithm = 4, random.seed = 42)

# Create DimPlot
p1 <- DimPlot(
  astro,
  reduction = "umap",
  group.by = c("stage", "New_cellType"),
  combine = FALSE, label.size = 2
)

p2 <- DimPlot(
  astro,
  reduction = "tsne",
  group.by = c("stage", "New_cellType"),
  combine = FALSE, label.size = 2
)

wrap_plots(c(p1, p2), ncol = 2, byrow = F)

Past versions of plot-stages-and-types-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

DimPlot(
  astro,
  reduction = "umap",
  group.by = c("astro_clusters"),
  combine = FALSE, label.size = 2,
  label = T
)

[[1]]

Past versions of plot-reclustered-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

# Create a custom FeaturePlot for each S100 gene
# plot_list <-
#     lapply(
#         c(s100_genes, "Cacybp", "Siah1a", "Siah1b"),
#         function(gene) {
#             FeaturePlot_scCustom(
#                 seurat_object = astro,
#                 features = gene,
#                 colors_use = merged_cortex@misc$expr_Colour_Pal,
#                 na_color = "lightgray",
#                 layer = "data",
#                 order = TRUE,
#                 pt.size = 1,
#                 reduction = "umap",
#                 split.by = "stage",
#                 split_collect = FALSE,
#                 label = F,
#                 label_feature_yaxis = TRUE,
#                 combine = FALSE
#             )
#         })
# 
# 
# # Combine the plots into a single grid
# combined_plot <- patchwork::wrap_plots(plot_list, ncol = 1)
# 
# # Display the combined plot
# print(combined_plot)

# Create a compact DotPlot
compact_plot <- DotPlot(
  object = astro,
  features = c(s100_genes,
               "Cacybp",
               "Siah1a",
               "Siah1b"),
  group.by = "stage",
  cluster.idents = F,
  scale = TRUE,
  dot.scale = 12
) + RotatedAxis()

# Display the compact plot
print(compact_plot)

Past versions of plot-s100-dot-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

# Create a compact DotPlot
compact_plot <- DotPlot(
  object = astro,
  features = c(s100_genes,
               "Cacybp",
               "Siah1a",
               "Siah1b"),
  group.by = "stage",
  cluster.idents = F,
  cols = c("yellow", "cyan", "magenta"),
  scale = TRUE,
  split.by = "New_cellType",
  dot.scale = 12
) + RotatedAxis()

# Display the compact plot
print(compact_plot)

Past versions of plot-s100-dot-split-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Gja1"))

Past versions of plot-density-s100a6-Gja1-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Gja1"))

Past versions of plot-density-CaCyBP-Gja1-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Glul"))

Past versions of plot-density-s100a6-Glul-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Glul"))

Past versions of plot-density-CaCyBP-Glul-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Apoe"))

Past versions of plot-density-s100a6-Apoe-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Apoe"))

Past versions of plot-density-CaCyBP-Apoe-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Ntrk2"))

Past versions of plot-density-s100a6-Ntrk2-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Ntrk2"))

Past versions of plot-density-CaCyBP-Ntrk2-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Ntsr2"))

Past versions of plot-density-s100a6-Ntsr2-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Ntsr2"))

Past versions of plot-density-CaCyBP-Ntsr2-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Ndrg2"))

Past versions of plot-density-s100a6-Ndrg2-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Ndrg2"))

Past versions of plot-density-CaCyBP-Ndrg2-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Aldoc"))

Past versions of plot-density-s100a6-Aldoc-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Aldoc"))

Past versions of plot-density-CaCyBP-Aldoc-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Slc1a3"))

Past versions of plot-density-s100a6-Slc1a3-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Slc1a3"))

Past versions of plot-density-CaCyBP-Slc1a3-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Gfap"))

Past versions of plot-density-s100a6-Gfap-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Gfap"))

Past versions of plot-density-CaCyBP-Gfap-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Htra1"))

Past versions of plot-density-s100a6-Htra1-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Htra1"))

Past versions of plot-density-CaCyBP-Htra1-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("S100a6", "Aqp4"))

Past versions of plot-density-s100a6-Aqp4-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

FeaturePlot(astro,
            blend = TRUE,
            features = c("Cacybp", "Aqp4"))

Past versions of plot-density-CaCyBP-Aqp4-astro-cells-1.png

Version	Author	Date
fd0e9b9	Evgenii O. Tretiakov	2024-06-09

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.2 (2023-10-31)
 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     2024-06-09
 pandoc   3.1.3 @ /opt/python/3.8.8/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package                     * version     date (UTC) lib source
 abind                         1.4-5       2016-07-21 [2] RSPM (R 4.3.0)
 annotate                      1.80.0      2023-10-24 [2] RSPM (R 4.3.2)
 AnnotationDbi                 1.64.1      2023-11-03 [2] RSPM (R 4.3.2)
 AnnotationFilter              1.26.0      2023-10-24 [2] RSPM (R 4.3.2)
 Azimuth                     * 0.5.0       2024-01-27 [2] Github (satijalab/azimuth@243ee5d)
 beeswarm                      0.4.0       2021-06-01 [2] RSPM (R 4.3.0)
 Biobase                       2.62.0      2023-10-24 [2] RSPM (R 4.3.2)
 BiocFileCache                 2.10.1      2023-10-26 [2] RSPM (R 4.3.2)
 BiocGenerics                  0.48.1      2023-11-01 [2] RSPM (R 4.3.2)
 BiocIO                        1.12.0      2023-10-24 [2] RSPM (R 4.3.2)
 BiocManager                   1.30.22     2023-08-08 [2] RSPM (R 4.3.0)
 BiocParallel                  1.36.0      2023-10-24 [2] RSPM (R 4.3.2)
 biomaRt                       2.58.0      2023-10-24 [2] RSPM (R 4.3.2)
 Biostrings                    2.70.1      2023-10-25 [2] RSPM (R 4.3.2)
 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)
 blob                          1.2.4       2023-03-17 [2] RSPM (R 4.3.0)
 BPCells                     * 0.1.0       2024-01-27 [2] Github (bnprks/BPCells@0d56524)
 BSgenome                      1.70.1      2023-11-01 [2] RSPM (R 4.3.2)
 BSgenome.Hsapiens.UCSC.hg38   1.4.5       2024-01-26 [2] RSPM (R 4.3.2)
 bslib                         0.6.1       2023-11-28 [2] RSPM (R 4.3.0)
 cachem                        1.0.8       2023-05-01 [2] RSPM (R 4.3.0)
 callr                         3.7.3       2022-11-02 [2] RSPM (R 4.3.0)
 caTools                       1.18.2      2021-03-28 [2] RSPM (R 4.3.0)
 cellranger                    1.1.0       2016-07-27 [2] RSPM (R 4.3.0)
 circlize                      0.4.16      2024-01-26 [2] Github (jokergoo/circlize@9b21578)
 cli                           3.6.2       2023-12-11 [2] RSPM (R 4.3.0)
 cluster                       2.1.6       2023-12-01 [2] RSPM (R 4.3.0)
 CNEr                          1.38.0      2023-10-24 [2] RSPM (R 4.3.2)
 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)
 cowplot                     * 1.1.3       2024-01-22 [2] RSPM (R 4.3.0)
 crayon                        1.5.2       2022-09-29 [2] RSPM (R 4.3.0)
 curl                          5.2.0       2023-12-08 [2] RSPM (R 4.3.0)
 data.table                    1.14.10     2023-12-08 [2] RSPM (R 4.3.0)
 DBI                           1.2.1       2024-01-12 [2] RSPM (R 4.3.0)
 dbplyr                        2.4.0       2023-10-26 [2] RSPM (R 4.3.0)
 DelayedArray                  0.28.0      2023-10-24 [2] RSPM (R 4.3.2)
 deldir                        2.0-2       2023-11-23 [2] RSPM (R 4.3.0)
 digest                        0.6.34      2024-01-11 [2] RSPM (R 4.3.0)
 DirichletMultinomial          1.44.0      2023-10-24 [2] RSPM (R 4.3.2)
 dotCall64                     1.1-1       2023-11-28 [2] RSPM (R 4.3.0)
 dplyr                       * 1.1.4       2023-11-17 [2] RSPM (R 4.3.0)
 DT                            0.31        2023-12-09 [2] RSPM (R 4.3.0)
 ellipsis                      0.3.2       2021-04-29 [2] RSPM (R 4.3.0)
 EnsDb.Hsapiens.v86            2.99.0      2024-01-26 [2] RSPM (R 4.3.2)
 ensembldb                     2.26.0      2023-10-24 [2] RSPM (R 4.3.2)
 evaluate                      0.23        2023-11-01 [2] RSPM (R 4.3.0)
 fansi                         1.0.6       2023-12-08 [2] RSPM (R 4.3.0)
 farver                        2.1.1       2022-07-06 [2] RSPM (R 4.3.0)
 fastDummies                   1.7.3       2023-07-06 [2] RSPM (R 4.3.0)
 fastmap                       1.1.1       2023-02-24 [2] RSPM (R 4.3.0)
 fastmatch                     1.1-4       2023-08-18 [2] RSPM (R 4.3.0)
 filelock                      1.0.3       2023-12-11 [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)
 fs                            1.6.3       2023-07-20 [2] RSPM (R 4.3.0)
 future                        1.33.1      2023-12-22 [2] RSPM (R 4.3.0)
 future.apply                  1.11.1      2023-12-21 [2] RSPM (R 4.3.0)
 gargle                        1.5.2       2023-07-20 [2] RSPM (R 4.3.0)
 generics                      0.1.3       2022-07-05 [2] RSPM (R 4.3.0)
 GenomeInfoDb                  1.38.5      2023-12-28 [2] RSPM (R 4.3.2)
 GenomeInfoDbData              1.2.11      2024-01-26 [2] RSPM (R 4.3.2)
 GenomicAlignments             1.38.2      2024-01-16 [2] RSPM (R 4.3.2)
 GenomicFeatures               1.54.1      2023-10-29 [2] RSPM (R 4.3.2)
 GenomicRanges                 1.54.1      2023-10-29 [2] RSPM (R 4.3.2)
 getPass                       0.2-4       2023-12-10 [2] RSPM (R 4.3.0)
 ggbeeswarm                    0.7.2       2024-01-26 [2] Github (eclarke/ggbeeswarm@3cf58a9)
 ggplot2                     * 3.4.4.9000  2024-01-26 [2] Github (tidyverse/ggplot2@a4be39d)
 ggprism                       1.0.4       2024-01-26 [2] Github (csdaw/ggprism@0e411f4)
 ggrastr                       1.0.2       2024-01-26 [2] Github (VPetukhov/ggrastr@50ca3e0)
 ggrepel                       0.9.5.9999  2024-01-26 [2] Github (slowkow/ggrepel@1144585)
 ggridges                      0.5.6       2024-01-23 [2] RSPM (R 4.3.0)
 git2r                         0.33.0      2023-11-26 [2] RSPM (R 4.3.0)
 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                          1.7.0       2024-01-09 [2] RSPM (R 4.3.0)
 GO.db                         3.18.0      2024-01-26 [2] RSPM (R 4.3.2)
 goftest                       1.2-3       2021-10-07 [2] RSPM (R 4.3.0)
 googledrive                   2.1.1       2023-06-11 [2] RSPM (R 4.3.0)
 googlesheets4                 1.1.1       2023-06-11 [2] RSPM (R 4.3.0)
 gridExtra                     2.3         2017-09-09 [2] RSPM (R 4.3.0)
 gtable                        0.3.4       2023-08-21 [2] RSPM (R 4.3.0)
 gtools                        3.9.5       2023-11-20 [2] RSPM (R 4.3.0)
 hdf5r                         1.3.9       2024-01-14 [2] RSPM (R 4.3.2)
 here                        * 1.0.1       2020-12-13 [2] RSPM (R 4.3.0)
 highr                         0.10        2022-12-22 [2] RSPM (R 4.3.0)
 hms                           1.1.3       2023-03-21 [2] RSPM (R 4.3.0)
 htmltools                     0.5.7       2023-11-03 [2] RSPM (R 4.3.0)
 htmlwidgets                   1.6.4       2023-12-06 [2] RSPM (R 4.3.0)
 httpuv                        1.6.13      2023-12-06 [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                        1.6.0       2023-12-11 [2] RSPM (R 4.3.0)
 IRanges                       2.36.0      2023-10-24 [2] RSPM (R 4.3.2)
 irlba                         2.3.5.1     2022-10-03 [2] RSPM (R 4.3.0)
 janitor                       2.2.0.9000  2024-01-26 [2] Github (sfirke/janitor@ad52765)
 JASPAR2020                    0.99.10     2024-01-26 [2] RSPM (R 4.3.2)
 jquerylib                     0.1.4       2021-04-26 [2] RSPM (R 4.3.0)
 jsonlite                      1.8.8       2023-12-04 [2] RSPM (R 4.3.0)
 KEGGREST                      1.42.0      2023-10-24 [2] RSPM (R 4.3.2)
 KernSmooth                    2.23-22     2023-07-10 [2] RSPM (R 4.3.0)
 knitr                         1.45        2023-10-30 [2] RSPM (R 4.3.0)
 labeling                      0.4.3       2023-08-29 [2] RSPM (R 4.3.0)
 later                         1.3.2       2023-12-06 [2] RSPM (R 4.3.0)
 lattice                       0.22-5      2023-10-24 [2] RSPM (R 4.3.0)
 lazyeval                      0.2.2       2019-03-15 [2] RSPM (R 4.3.0)
 leiden                        0.4.3.1     2023-11-17 [2] RSPM (R 4.3.0)
 lifecycle                     1.0.4       2023-11-07 [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                     1.9.3       2023-09-27 [2] RSPM (R 4.3.0)
 magrittr                    * 2.0.3       2022-03-30 [2] RSPM (R 4.3.0)
 MASS                          7.3-60.0.1  2024-01-13 [2] RSPM (R 4.3.0)
 Matrix                        1.6-5       2024-01-11 [2] RSPM (R 4.3.0)
 MatrixGenerics                1.14.0      2023-10-24 [2] RSPM (R 4.3.2)
 matrixStats                   1.2.0       2023-12-11 [2] RSPM (R 4.3.0)
 memoise                       2.0.1       2021-11-26 [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)
 mousecortexref.SeuratData   * 1.0.0       2023-10-20 [1] local
 munsell                       0.5.0       2018-06-12 [2] RSPM (R 4.3.0)
 nlme                          3.1-164     2023-11-27 [2] RSPM (R 4.3.0)
 paletteer                     1.6.0       2024-01-21 [2] RSPM (R 4.3.0)
 parallelly                    1.36.0      2023-05-26 [2] RSPM (R 4.3.0)
 patchwork                   * 1.2.0.9000  2024-01-26 [2] Github (thomasp85/patchwork@d943757)
 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                        4.10.4      2024-01-13 [2] RSPM (R 4.3.0)
 plyr                          1.8.9       2023-10-02 [2] RSPM (R 4.3.0)
 png                           0.1-8       2022-11-29 [2] RSPM (R 4.3.0)
 polyclip                      1.10-6      2023-09-27 [2] RSPM (R 4.3.0)
 poweRlaw                      0.80.0      2024-01-25 [2] RSPM (R 4.3.2)
 pracma                        2.4.4       2023-11-10 [2] RSPM (R 4.3.0)
 presto                        1.0.0       2024-01-26 [2] Github (immunogenomics/presto@31dc97f)
 prettyunits                   1.2.0       2023-09-24 [2] RSPM (R 4.3.0)
 processx                      3.8.3       2023-12-10 [2] RSPM (R 4.3.0)
 progress                      1.2.3       2023-12-06 [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)
 ProtGenerics                  1.34.0      2023-10-24 [2] RSPM (R 4.3.2)
 ps                            1.7.6       2024-01-18 [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                          1.26.0      2024-01-24 [2] RSPM (R 4.3.0)
 R.utils                       2.12.3      2023-11-18 [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)
 rappdirs                      0.3.3       2021-01-31 [2] RSPM (R 4.3.0)
 RColorBrewer                * 1.1-3       2022-04-03 [2] RSPM (R 4.3.0)
 Rcpp                          1.0.12      2024-01-09 [2] RSPM (R 4.3.0)
 RcppAnnoy                     0.0.22      2024-01-23 [2] RSPM (R 4.3.0)
 RcppHNSW                      0.5.0       2023-09-19 [2] RSPM (R 4.3.0)
 RcppRoll                      0.3.0       2018-06-05 [2] RSPM (R 4.3.0)
 RCurl                         1.98-1.14   2024-01-09 [2] RSPM (R 4.3.0)
 readr                       * 2.1.5       2024-01-10 [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)
 reshape2                      1.4.4       2020-04-09 [2] RSPM (R 4.3.0)
 restfulr                      0.0.15      2022-06-16 [2] RSPM (R 4.3.2)
 reticulate                    1.34.0      2023-10-12 [2] RSPM (R 4.3.0)
 rhdf5                         2.46.1      2023-11-29 [2] RSPM (R 4.3.2)
 rhdf5filters                  1.14.1      2023-11-06 [2] RSPM (R 4.3.2)
 Rhdf5lib                      1.24.1      2023-12-11 [2] RSPM (R 4.3.2)
 rjson                         0.2.21      2022-01-09 [2] RSPM (R 4.3.0)
 rlang                         1.1.3       2024-01-10 [2] RSPM (R 4.3.0)
 rmarkdown                     2.25        2023-09-18 [2] RSPM (R 4.3.0)
 ROCR                          1.0-11      2020-05-02 [2] RSPM (R 4.3.0)
 rprojroot                     2.0.4       2023-11-05 [2] RSPM (R 4.3.0)
 Rsamtools                     2.18.0      2023-10-24 [2] RSPM (R 4.3.2)
 RSpectra                      0.16-1      2022-04-24 [2] RSPM (R 4.3.0)
 RSQLite                       2.3.5       2024-01-21 [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)
 rtracklayer                   1.62.0      2024-01-26 [2] bioc_git2r (@58efbf9)
 Rtsne                         0.17        2023-12-07 [2] RSPM (R 4.3.0)
 S4Arrays                      1.2.0       2023-10-24 [2] RSPM (R 4.3.2)
 S4Vectors                     0.40.2      2023-11-23 [2] RSPM (R 4.3.2)
 sass                          0.4.8       2023-12-06 [2] RSPM (R 4.3.0)
 scales                        1.3.0       2023-11-28 [2] RSPM (R 4.3.0)
 scattermore                   1.2         2023-06-12 [2] RSPM (R 4.3.0)
 scCustomize                 * 2.0.1       2024-01-26 [2] Github (samuel-marsh/scCustomize@0aefbe9)
 sctransform                   0.4.1       2023-10-19 [2] RSPM (R 4.3.0)
 seqLogo                       1.68.0      2023-10-24 [2] RSPM (R 4.3.2)
 sessioninfo                   1.2.2       2021-12-06 [2] RSPM (R 4.3.0)
 Seurat                      * 5.0.1.9003  2024-01-27 [2] Github (satijalab/seurat@938698c)
 SeuratData                  * 0.2.2.9001  2024-01-26 [2] Github (satijalab/seurat-data@4dc08e0)
 SeuratDisk                    0.0.0.9021  2024-01-26 [2] Github (mojaveazure/seurat-disk@877d4e1)
 SeuratObject                * 5.0.1       2024-01-27 [2] Github (satijalab/seurat-object@4d3739b)
 SeuratWrappers              * 0.3.3       2024-01-26 [2] Github (satijalab/seurat-wrappers@17b8d5a)
 shape                         1.4.6       2021-05-19 [2] RSPM (R 4.3.0)
 shiny                         1.8.0       2023-11-17 [2] RSPM (R 4.3.0)
 shinyBS                     * 0.61.1      2022-04-17 [2] RSPM (R 4.3.0)
 shinydashboard                0.7.2       2021-09-30 [2] RSPM (R 4.3.0)
 shinyjs                       2.1.0       2021-12-23 [2] RSPM (R 4.3.0)
 Signac                        1.12.9004   2024-01-27 [2] Github (stuart-lab/signac@0c43d88)
 snakecase                     0.11.1      2023-08-27 [2] RSPM (R 4.3.0)
 sp                          * 2.1-2       2023-11-26 [2] RSPM (R 4.3.0)
 spam                          2.10-0      2023-10-23 [2] RSPM (R 4.3.0)
 SparseArray                   1.2.3       2023-12-25 [2] RSPM (R 4.3.2)
 spatstat.data                 3.0-4       2024-01-15 [2] RSPM (R 4.3.0)
 spatstat.explore              3.2-5       2023-10-22 [2] RSPM (R 4.3.0)
 spatstat.geom                 3.2-7       2023-10-20 [2] RSPM (R 4.3.0)
 spatstat.random               3.2-2       2023-11-29 [2] RSPM (R 4.3.0)
 spatstat.sparse               3.0-3       2023-10-24 [2] RSPM (R 4.3.0)
 spatstat.utils                3.0-4       2023-10-24 [2] RSPM (R 4.3.0)
 stringi                       1.8.3       2023-12-11 [2] RSPM (R 4.3.0)
 stringr                     * 1.5.1       2023-11-14 [2] RSPM (R 4.3.0)
 SummarizedExperiment          1.32.0      2023-10-24 [2] RSPM (R 4.3.2)
 survival                      3.5-7       2023-08-14 [2] RSPM (R 4.3.0)
 tensor                        1.5         2012-05-05 [2] RSPM (R 4.3.0)
 TFBSTools                     1.40.0      2023-10-24 [2] RSPM (R 4.3.2)
 TFMPvalue                     0.0.9       2022-10-21 [2] RSPM (R 4.3.0)
 tibble                        3.2.1       2023-03-20 [2] RSPM (R 4.3.0)
 tidyr                         1.3.1       2024-01-24 [2] RSPM (R 4.3.0)
 tidyselect                    1.2.0       2022-10-10 [2] RSPM (R 4.3.0)
 timechange                    0.3.0       2024-01-18 [2] RSPM (R 4.3.0)
 tzdb                          0.4.0       2023-05-12 [2] RSPM (R 4.3.0)
 utf8                          1.2.4       2023-10-22 [2] RSPM (R 4.3.0)
 uwot                          0.1.16      2023-06-29 [2] RSPM (R 4.3.0)
 vctrs                         0.6.5       2023-12-01 [2] RSPM (R 4.3.0)
 vipor                         0.4.7       2023-12-18 [2] RSPM (R 4.3.0)
 viridisLite                   0.4.2       2023-05-02 [2] RSPM (R 4.3.0)
 vroom                         1.6.5       2023-12-05 [2] RSPM (R 4.3.0)
 whisker                       0.4.1       2022-12-05 [2] RSPM (R 4.3.0)
 withr                         3.0.0       2024-01-16 [2] RSPM (R 4.3.0)
 workflowr                   * 1.7.1       2023-08-23 [2] RSPM (R 4.3.0)
 xfun                          0.41        2023-11-01 [2] RSPM (R 4.3.0)
 XML                           3.99-0.16.1 2024-01-22 [2] RSPM (R 4.3.0)
 xml2                          1.3.6       2023-12-04 [2] RSPM (R 4.3.0)
 xtable                        1.8-4       2019-04-21 [2] RSPM (R 4.3.0)
 XVector                       0.42.0      2023-10-24 [2] RSPM (R 4.3.2)
 yaml                          2.3.8       2023-12-11 [2] RSPM (R 4.3.0)
 zlibbioc                      1.48.0      2023-10-24 [2] RSPM (R 4.3.2)
 zoo                           1.8-12      2023-04-13 [2] RSPM (R 4.3.0)

 [1] /home/etretiakov/R/x86_64-pc-linux-gnu-library/4.3
 [2] /opt/R/4.3.2/lib/R/library

─ Python configuration ───────────────────────────────────────────────────────
 python:         /opt/python/3.8.8/bin/python
 libpython:      /opt/python/3.8.8/lib/libpython3.8.so
 pythonhome:     /opt/python/3.8.8:/opt/python/3.8.8
 version:        3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]
 numpy:          /opt/python/3.8.8/lib/python3.8/site-packages/numpy
 numpy_version:  1.23.5
 leidenalg:      /opt/python/3.8.8/lib/python3.8/site-packages/leidenalg
 
 NOTE: Python version was forced by RETICULATE_PYTHON

──────────────────────────────────────────────────────────────────────────────

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 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;  LAPACK version 3.10.0

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       

time zone: Etc/UTC
tzcode source: system (glibc)

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

other attached packages:
 [1] patchwork_1.2.0.9000            cowplot_1.1.3                  
 [3] ggplot2_3.4.4.9000              readr_2.1.5                    
 [5] stringr_1.5.1                   magrittr_2.0.3                 
 [7] dplyr_1.1.4                     BPCells_0.1.0                  
 [9] Azimuth_0.5.0                   shinyBS_0.61.1                 
[11] SeuratWrappers_0.3.3            mousecortexref.SeuratData_1.0.0
[13] SeuratData_0.2.2.9001           scCustomize_2.0.1              
[15] Seurat_5.0.1.9003               SeuratObject_5.0.1             
[17] sp_2.1-2                        RColorBrewer_1.1-3             
[19] here_1.0.1                      workflowr_1.7.1                

loaded via a namespace (and not attached):
  [1] IRanges_2.36.0                    R.methodsS3_1.8.2                
  [3] vroom_1.6.5                       progress_1.2.3                   
  [5] poweRlaw_0.80.0                   goftest_1.2-3                    
  [7] DT_0.31                           Biostrings_2.70.1                
  [9] vctrs_0.6.5                       spatstat.random_3.2-2            
 [11] digest_0.6.34                     png_0.1-8                        
 [13] shape_1.4.6                       git2r_0.33.0                     
 [15] ggrepel_0.9.5.9999                deldir_2.0-2                     
 [17] parallelly_1.36.0                 MASS_7.3-60.0.1                  
 [19] Signac_1.12.9004                  reshape2_1.4.4                   
 [21] httpuv_1.6.13                     BiocGenerics_0.48.1              
 [23] withr_3.0.0                       ggrastr_1.0.2                    
 [25] xfun_0.41                         ellipsis_0.3.2                   
 [27] survival_3.5-7                    EnsDb.Hsapiens.v86_2.99.0        
 [29] memoise_2.0.1                     ggbeeswarm_0.7.2                 
 [31] janitor_2.2.0.9000                zoo_1.8-12                       
 [33] GlobalOptions_0.1.2               gtools_3.9.5                     
 [35] pbapply_1.7-2                     R.oo_1.26.0                      
 [37] prettyunits_1.2.0                 rematch2_2.1.2                   
 [39] KEGGREST_1.42.0                   promises_1.2.1                   
 [41] httr_1.4.7                        restfulr_0.0.15                  
 [43] rhdf5filters_1.14.1               globals_0.16.2                   
 [45] fitdistrplus_1.1-11               rhdf5_2.46.1                     
 [47] ps_1.7.6                          rstudioapi_0.15.0                
 [49] miniUI_0.1.1.1                    generics_0.1.3                   
 [51] processx_3.8.3                    curl_5.2.0                       
 [53] S4Vectors_0.40.2                  zlibbioc_1.48.0                  
 [55] polyclip_1.10-6                   GenomeInfoDbData_1.2.11          
 [57] SparseArray_1.2.3                 xtable_1.8-4                     
 [59] pracma_2.4.4                      evaluate_0.23                    
 [61] S4Arrays_1.2.0                    BiocFileCache_2.10.1             
 [63] hms_1.1.3                         GenomicRanges_1.54.1             
 [65] irlba_2.3.5.1                     colorspace_2.1-0                 
 [67] filelock_1.0.3                    hdf5r_1.3.9                      
 [69] ROCR_1.0-11                       reticulate_1.34.0                
 [71] spatstat.data_3.0-4               lmtest_0.9-40                    
 [73] snakecase_0.11.1                  later_1.3.2                      
 [75] lattice_0.22-5                    spatstat.geom_3.2-7              
 [77] future.apply_1.11.1               getPass_0.2-4                    
 [79] scattermore_1.2                   XML_3.99-0.16.1                  
 [81] matrixStats_1.2.0                 RcppAnnoy_0.0.22                 
 [83] pillar_1.9.0                      nlme_3.1-164                     
 [85] caTools_1.18.2                    compiler_4.3.2                   
 [87] RSpectra_0.16-1                   stringi_1.8.3                    
 [89] tensor_1.5                        SummarizedExperiment_1.32.0      
 [91] lubridate_1.9.3                   GenomicAlignments_1.38.2         
 [93] plyr_1.8.9                        crayon_1.5.2                     
 [95] abind_1.4-5                       BiocIO_1.12.0                    
 [97] googledrive_2.1.1                 bit_4.0.5                        
 [99] fastmatch_1.1-4                   whisker_0.4.1                    
[101] codetools_0.2-19                  bslib_0.6.1                      
[103] paletteer_1.6.0                   plotly_4.10.4                    
[105] mime_0.12                         splines_4.3.2                    
[107] circlize_0.4.16                   Rcpp_1.0.12                      
[109] fastDummies_1.7.3                 dbplyr_2.4.0                     
[111] cellranger_1.1.0                  knitr_1.45                       
[113] blob_1.2.4                        utf8_1.2.4                       
[115] seqLogo_1.68.0                    AnnotationFilter_1.26.0          
[117] fs_1.6.3                          listenv_0.9.0                    
[119] tibble_3.2.1                      Matrix_1.6-5                     
[121] callr_3.7.3                       tzdb_0.4.0                       
[123] pkgconfig_2.0.3                   tools_4.3.2                      
[125] cachem_1.0.8                      RSQLite_2.3.5                    
[127] viridisLite_0.4.2                 DBI_1.2.1                        
[129] fastmap_1.1.1                     rmarkdown_2.25                   
[131] scales_1.3.0                      grid_4.3.2                       
[133] ica_1.0-3                         shinydashboard_0.7.2             
[135] Rsamtools_2.18.0                  sass_0.4.8                       
[137] ggprism_1.0.4                     BiocManager_1.30.22              
[139] dotCall64_1.1-1                   RANN_2.6.1                       
[141] farver_2.1.1                      yaml_2.3.8                       
[143] MatrixGenerics_1.14.0             rtracklayer_1.62.0               
[145] cli_3.6.2                         purrr_1.0.2                      
[147] stats4_4.3.2                      leiden_0.4.3.1                   
[149] lifecycle_1.0.4                   uwot_0.1.16                      
[151] Biobase_2.62.0                    sessioninfo_1.2.2                
[153] presto_1.0.0                      BSgenome.Hsapiens.UCSC.hg38_1.4.5
[155] BiocParallel_1.36.0               annotate_1.80.0                  
[157] timechange_0.3.0                  gtable_0.3.4                     
[159] rjson_0.2.21                      ggridges_0.5.6                   
[161] progressr_0.14.0                  parallel_4.3.2                   
[163] jsonlite_1.8.8                    RcppHNSW_0.5.0                   
[165] TFBSTools_1.40.0                  bitops_1.0-7                     
[167] bit64_4.0.5                       Rtsne_0.17                       
[169] spatstat.utils_3.0-4              CNEr_1.38.0                      
[171] highr_0.10                        jquerylib_0.1.4                  
[173] shinyjs_2.1.0                     SeuratDisk_0.0.0.9021            
[175] R.utils_2.12.3                    lazyeval_0.2.2                   
[177] shiny_1.8.0                       htmltools_0.5.7                  
[179] GO.db_3.18.0                      sctransform_0.4.1                
[181] rappdirs_0.3.3                    ensembldb_2.26.0                 
[183] glue_1.7.0                        TFMPvalue_0.0.9                  
[185] spam_2.10-0                       googlesheets4_1.1.1              
[187] XVector_0.42.0                    RCurl_1.98-1.14                  
[189] rprojroot_2.0.4                   BSgenome_1.70.1                  
[191] gridExtra_2.3                     JASPAR2020_0.99.10               
[193] igraph_1.6.0                      R6_2.5.1                         
[195] tidyr_1.3.1                       labeling_0.4.3                   
[197] forcats_1.0.0                     RcppRoll_0.3.0                   
[199] GenomicFeatures_1.54.1            cluster_2.1.6                    
[201] Rhdf5lib_1.24.1                   gargle_1.5.2                     
[203] GenomeInfoDb_1.38.5               DirichletMultinomial_1.44.0      
[205] DelayedArray_0.28.0               tidyselect_1.2.0                 
[207] vipor_0.4.7                       ProtGenerics_1.34.0              
[209] xml2_1.3.6                        AnnotationDbi_1.64.1             
[211] future_1.33.1                     rsvd_1.0.5                       
[213] munsell_0.5.0                     KernSmooth_2.23-22               
[215] data.table_1.14.10                htmlwidgets_1.6.4                
[217] biomaRt_2.58.0                    rlang_1.1.3                      
[219] spatstat.sparse_3.0-3             spatstat.explore_3.2-5           
[221] remotes_2.4.2.1                   fansi_1.0.6                      
[223] beeswarm_0.4.0