Nasal_brushings

Clustering and Marker gene analysis

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Last updated: 2024-07-26

Checks: 6 1

Knit directory: paed-airway-allTissues/

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

File	Version	Author	Date	Message
Rmd	c20f60f	Gunjan Dixit	2024-07-08	Updated marker gene dot plots
html	c20f60f	Gunjan Dixit	2024-07-08	Updated marker gene dot plots
Rmd	77c742e	Gunjan Dixit	2024-06-26	Updated RMarkdown files of all Tissues
html	77c742e	Gunjan Dixit	2024-06-26	Updated RMarkdown files of all Tissues
Rmd	4fa7db5	Gunjan Dixit	2024-06-13	Updated Nasal_brushing
html	4fa7db5	Gunjan Dixit	2024-06-13	Updated Nasal_brushing
Rmd	db794c0	Gunjan Dixit	2024-06-13	Updated Nasal_brushing.Rmd
html	db794c0	Gunjan Dixit	2024-06-13	Updated Nasal_brushing.Rmd
Rmd	e0e83af	Gunjan Dixit	2024-06-04	Updated reclustering
html	e0e83af	Gunjan Dixit	2024-06-04	Updated reclustering
html	fa7b973	Gunjan Dixit	2024-05-01	Modified/Annotated RMarkdown files
Rmd	320ccbd	Gunjan Dixit	2024-05-01	Modified/Annotated RMarkdown files
html	320ccbd	Gunjan Dixit	2024-05-01	Modified/Annotated RMarkdown files
Rmd	9492583	Gunjan Dixit	2024-04-26	Added new analysis
html	9492583	Gunjan Dixit	2024-04-26	Added new analysis

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Introduction

This Rmarkdown file loads and analyzes the batch-integrated/merged Seurat object for Nasal Brushings (Batch1 and Batch5). It performs clustering at various resolutions ranging from 0-1, followed by visualization of identified clusters and Broad Level 3 cell labels on UMAP. Next, the FindAllMarkers function is used to perform marker gene analysis to identify marker genes for each cluster. The top marker gene is visualized using FeaturePlot, ViolinPlot and Heatmap. The identified marker genes are stored in CSV format for each cluster at the optimum resolution identified using clustree function.

Load libraries

suppressPackageStartupMessages({
  library(BiocStyle)
  library(tidyverse)
  library(here)
  library(glue)
  library(dplyr)
  library(Seurat)
  library(clustree)
  library(kableExtra)
  library(RColorBrewer)
  library(data.table)
  library(ggplot2)
  library(patchwork)
  library(limma)
  library(edgeR)
  library(speckle)
  library(AnnotationDbi)
  library(org.Hs.eg.db)
  library(readxl)
})

Load Input data

Load merged object (batch corrected/integrated) for the tissue.

tissue <- "Nasal_brushings"
out <- here("output/RDS/AllBatches_Harmony_SEUs/G000231_Neeland_Nasal_brushings_batchCorrection.Harmony.clusters.SEU.rds")
merged_obj <- readRDS(out)
merged_obj

An object of class Seurat 
17973 features across 74812 samples within 1 assay 
Active assay: RNA (17973 features, 2000 variable features)
 5 layers present: counts.G000231_batch1, counts.G000231_batch5, scale.data, data.G000231_batch1, data.G000231_batch5
 4 dimensional reductions calculated: pca, umap.unintegrated, harmony, umap.harmony

Clustering

Clustering is done on the “harmony” or batch integrated reduction at resolutions ranging from 0-1.

out1 <- here("output",
            "RDS", "AllBatches_Clustering_SEUs",
             paste0("G000231_Neeland_",tissue,".Clusters.SEU.rds"))
#dir.create(out1)

resolutions <- seq(0.1, 1, by = 0.1)
if (!file.exists(out1)) {
merged_obj <- FindNeighbors(merged_obj, reduction = "harmony", dims = 1:30)
merged_obj <- FindClusters(merged_obj, resolution = seq(0.1, 1, by = 0.1), algorithm = 3)
saveRDS(merged_obj, file = out1)
} else {
    merged_obj <- readRDS(out1)
}

The clustree function is used to visualize the clustering at different resolutions to identify the most optimum resolution.

clustree(merged_obj, prefix = "RNA_snn_res.")

Past versions of clustree-1.png

Version	Author	Date
9492583	Gunjan Dixit	2024-04-26

Based on the clustering tree, we chose an intermediate/optimum resolution of 0.4 where the clustering results are the most stable, with the least amount of shuffling cells.

opt_res <- "RNA_snn_res.0.4"  
n <- nlevels(merged_obj$RNA_snn_res.0.4)
merged_obj$RNA_snn_res.0.4 <- factor(merged_obj$RNA_snn_res.0.4, levels = seq(0,n-1))
merged_obj$seurat_clusters <- NULL
merged_obj$cluster <- merged_obj$RNA_snn_res.0.4
Idents(merged_obj) <- merged_obj$cluster

UMAP after clustering

Defining colours for each cell-type to be consistent with other age-related/cell type composition plots.

my_colors <- c(
  "B cells" = "steelblue",
  "CD4 T cells" = "brown",
  "Double negative T cells" = "gold",
  "CD8 T cells" = "lightgreen",
  "Pre B/T cells" = "orchid",
  "Innate lymphoid cells" = "tan",
  "Natural Killer cells" = "blueviolet",
  "Macrophages" = "green4",
  "Cycling T cells" = "turquoise",
  "Dendritic cells" = "grey80",
  "Gamma delta T cells" = "mediumvioletred",
  "Epithelial lineage" = "darkorange",
  "Granulocytes" = "olivedrab",
  "Fibroblast lineage" = "lavender",
  "None" = "white",
  "Monocytes" = "peachpuff",
  "Endothelial lineage" = "cadetblue",
  "SMG duct" = "lightpink",
  "Neuroendocrine" = "skyblue",
  "Doublet query/Other" = "#d62728"
)

UMAP displaying clusters at opt_res resolution and Broad cell Labels Level 3.

p1 <- DimPlot(merged_obj, reduction = "umap.harmony", raster = FALSE ,repel = TRUE, label = TRUE,label.size = 3.5, group.by = opt_res) +  NoLegend()

p2 <- DimPlot(merged_obj, reduction = "umap.harmony", raster = FALSE, repel = TRUE, label = TRUE, label.size = 3.5, group.by = "Broad_cell_label_3")  +
  scale_colour_manual(values = my_colors) + 
  ggtitle(paste0(tissue, ": Batch Corrected UMAP")) 


p1 / p2 

Past versions of umap_after_clustering-1.png

Version	Author	Date
fa7b973	Gunjan Dixit	2024-05-01
320ccbd	Gunjan Dixit	2024-05-01
9492583	Gunjan Dixit	2024-04-26

Save batch corrected Object

out1 <- here("output",
            "RDS", "AllBatches_Clustering_SEUs",
             paste0("G000231_Neeland_",tissue,".Clusters.SEU.rds"))
#dir.create(out1)
if (!file.exists(out1)) {
  saveRDS(merged_obj, file = out1)
}

Marker Gene Analysis

merged_obj <- JoinLayers(merged_obj)
paed.markers <- FindAllMarkers(merged_obj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

Extracting top 5 genes per cluster for visualization. The ‘top5’ contains the top 5 genes with the highest weighted average avg_log2FC within each cluster and the ‘best.wilcox.gene.per.cluster’ contains the single best gene with the highest weighted average avg_log2FC for each cluster.

paed.markers %>%
    group_by(cluster) %>% unique() %>% 
    top_n(n = 5, wt = avg_log2FC) -> top5

paed.markers %>%
    group_by(cluster) %>% 
    slice_head(n=1) %>% 
    pull(gene) -> best.wilcox.gene.per.cluster

best.wilcox.gene.per.cluster

 [1] "CD3D"     "CD79A"    "EPAS1"    "ALPL"     "TMEM190"  "NKG7"    
 [7] "C20orf85" "SPI1"     "CXCL10"   "FOSB"     "CDC20B"   "CSF3R"   
[13] "CXCL8"    "FRMPD2"   "TK1"      "CD79B"    "LMNB1"    "HBB"     
[19] "CPA3"     "FOXI1"    "LILRA4"   "MLANA"    "CSF3R"    "BEST4"   

Marker gene expression in clusters

This heatmap depicts the expression of top five genes in each cluster.

DoHeatmap(merged_obj, features = top5$gene) + NoLegend()

Past versions of top5_markers_heatmap-1.png

Version	Author	Date
320ccbd	Gunjan Dixit	2024-05-01

Violin plot shows the expression of top marker gene per cluster.

VlnPlot(merged_obj, features=best.wilcox.gene.per.cluster, ncol = 2, raster = FALSE, pt.size = FALSE)

Past versions of top_marker_violinplot-1.png

Version	Author	Date
320ccbd	Gunjan Dixit	2024-05-01

Violin plot shows the expression of top marker gene per cluster and compares its expression in both batches.

plots <- VlnPlot(merged_obj, features = best.wilcox.gene.per.cluster, split.by = "batch_name", group.by = "Broad_cell_label_3",
    pt.size = 0, combine = FALSE, raster = FALSE, split.plot = TRUE)

The default behaviour of split.by has changed.
Separate violin plots are now plotted side-by-side.
To restore the old behaviour of a single split violin,
set split.plot = TRUE.
      
This message will be shown once per session.

wrap_plots(plots = plots, ncol = 1)

Past versions of marker_batch_violinplot-1.png

Version	Author	Date
320ccbd	Gunjan Dixit	2024-05-01

Feature plot shows the expression of top marker genes per cluster.

FeaturePlot(merged_obj,features=best.wilcox.gene.per.cluster, reduction = 'umap.harmony', raster = FALSE, ncol = 2)

Past versions of top_marker_featureplot-1.png

Version	Author	Date
320ccbd	Gunjan Dixit	2024-05-01

Extract markers for each cluster

This section extracts marker genes for each cluster and save them as a CSV file.

out_markers <- here("output",
            "CSV", 
            paste(tissue,"_Marker_gene_clusters.",opt_res, sep = ""))

dir.create(out_markers, recursive = TRUE, showWarnings = FALSE)

for (cl in unique(paed.markers$cluster)) {
  cluster_data <- paed.markers %>% dplyr::filter(cluster == cl)
  file_name <- here(out_markers, paste0("G000231_Neeland_",tissue, "_cluster_", cl, ".csv"))
  write.csv(cluster_data, file = file_name)
}

Updated cell-type labels

cell_labels <- readxl::read_excel(here("data/Cell_labels_Mel/earlyAIR_nasal_brushing_annotations_02.05.24.xlsx"))
new_cluster_names <- cell_labels %>% 
  dplyr::select(cluster, annotation) %>% 
  deframe()

merged_obj <- RenameIdents(merged_obj, new_cluster_names)
merged_obj@meta.data$cell_labels <- Idents(merged_obj)

p3 <- DimPlot(merged_obj, reduction = "umap.harmony", raster = FALSE, repel = TRUE, label = TRUE, label.size = 3.5) + ggtitle(paste0(tissue, ": UMAP with Updated cell types"))

p3

Past versions of unnamed-chunk-1-1.png

Version	Author	Date
e0e83af	Gunjan Dixit	2024-06-04

merged_obj@meta.data %>%
  ggplot(aes(x = cell_labels, fill = cell_labels)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count",
            vjust = -0.5, colour = "black", size = 2) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  NoLegend() + ggtitle(paste0(tissue, " : Counts per cell-type"))

Past versions of unnamed-chunk-2-1.png

Version	Author	Date
e0e83af	Gunjan Dixit	2024-06-04

Reclustering of Goblet/Club/Basal cells

The marker genes for this reclustering can be found here-

NB_Basal_population_res.0.1

idx <- which(Idents(merged_obj) %in% "goblet/club/basal cells")
paed_sub <- merged_obj[,idx]
mito_genes <- grep("^MT-", rownames(paed_sub), value = TRUE)
paed_sub <- subset(paed_sub, features = setdiff(rownames(paed_sub), mito_genes))
paed_sub

An object of class Seurat 
17962 features across 19899 samples within 1 assay 
Active assay: RNA (17962 features, 2000 variable features)
 3 layers present: data, counts, scale.data
 4 dimensional reductions calculated: pca, umap.unintegrated, harmony, umap.harmony

paed_sub <- paed_sub %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() 
  
paed_sub <- RunUMAP(paed_sub, dims = 1:30, reduction = "pca", reduction.name = "umap.new")

meta_data_columns <- colnames(paed_sub@meta.data)
columns_to_remove <- grep("^RNA_snn_res", meta_data_columns, value = TRUE)
paed_sub@meta.data <- paed_sub@meta.data[, !(colnames(paed_sub@meta.data) %in% columns_to_remove)]


resolutions <- seq(0.1, 1, by = 0.1)
paed_sub <- FindNeighbors(paed_sub, reduction = "pca", dims = 1:30)
paed_sub <- FindClusters(paed_sub, resolution = resolutions, algorithm = 3)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19899
Number of edges: 690634

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9405
Number of communities: 6
Elapsed time: 12 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19899
Number of edges: 690634

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9184
Number of communities: 9
Elapsed time: 10 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19899
Number of edges: 690634

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9058
Number of communities: 12
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19899
Number of edges: 690634

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8963
Number of communities: 16
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19899
Number of edges: 690634

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8878
Number of communities: 16
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19899
Number of edges: 690634

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8795
Number of communities: 18
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19899
Number of edges: 690634

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8723
Number of communities: 21
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19899
Number of edges: 690634

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8664
Number of communities: 23
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19899
Number of edges: 690634

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8605
Number of communities: 24
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19899
Number of edges: 690634

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8550
Number of communities: 25
Elapsed time: 8 seconds

DimHeatmap(paed_sub, dims = 1:10, cells = 500, balanced = TRUE)

Past versions of reclustering-1.png

Version	Author	Date
e0e83af	Gunjan Dixit	2024-06-04

clustree(paed_sub, prefix = "RNA_snn_res.")

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
db794c0	Gunjan Dixit	2024-06-13

opt_res <- "RNA_snn_res.0.1"  
n <- nlevels(paed_sub$RNA_snn_res.0.1)
paed_sub$RNA_snn_res.0.1 <- factor(paed_sub$RNA_snn_res.0.1, levels = seq(0,n-1))
paed_sub$seurat_clusters <- NULL
Idents(paed_sub) <- paed_sub$RNA_snn_res.0.1

DimPlot(paed_sub, reduction = "umap.new", group.by = opt_res, label = TRUE, label.size = 4.5, repel = TRUE, raster = FALSE )

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
db794c0	Gunjan Dixit	2024-06-13
e0e83af	Gunjan Dixit	2024-06-04
9492583	Gunjan Dixit	2024-04-26

paed_sub.markers <- FindAllMarkers(paed_sub, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

Calculating cluster 0

Calculating cluster 1

Calculating cluster 2

Calculating cluster 3

Calculating cluster 4

Calculating cluster 5

paed_sub.markers %>%
    group_by(cluster) %>% unique() %>% 
    top_n(n = 5, wt = avg_log2FC) -> top5

paed_sub.markers %>%
    group_by(cluster) %>% 
    slice_head(n=1) %>% 
    pull(gene) -> best.wilcox.gene.per.cluster

best.wilcox.gene.per.cluster

[1] "ADAM28"    "SPNS2"     "FOSB"      "TTC9"      "SLC34A2"   "TMPRSS11E"

FeaturePlot(paed_sub,features=best.wilcox.gene.per.cluster, reduction = 'umap.new', raster = FALSE, label = T, ncol = 2)

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
db794c0	Gunjan Dixit	2024-06-13

out_markers <- here("output",
            "CSV", 
            paste(tissue,"_Marker_genes_Reclustered_Basal_population.",opt_res, sep = ""))

dir.create(out_markers, recursive = TRUE, showWarnings = FALSE)

for (cl in unique(paed_sub.markers$cluster)) {
  cluster_data <- paed_sub.markers %>% dplyr::filter(cluster == cl)
  file_name <- here(out_markers, paste0("G000231_Neeland_",tissue, "_cluster_", cl, ".csv"))
  write.csv(cluster_data, file = file_name)
}

Expression of known marker genes

Goblet cells (Bronchial, Nasal and subsegmental)

LYPD2 and PSCA are specific to Nasal. BPIFB1, C16orf89 and NPDC1 are specific to subsegmental.

known_markers <- c("FXYD3","EPCAM", "ELF3", "IGFBP2", "SERPINF1", "TSPAN1", "GPX8", "ALDH1A3", "CEACAM5", "LYPD2", "PSCA", "BPIFB1", "C16orf89", "NPDC1")
FeaturePlot(paed_sub,features=known_markers, reduction = 'umap.new', raster = FALSE, label = T, ncol = 3)

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
db794c0	Gunjan Dixit	2024-06-13
e0e83af	Gunjan Dixit	2024-06-04

Club cells

club_markers <- c("SERPINB3", "TCN1", "ASRGL1")
FeaturePlot(paed_sub,features=club_markers, reduction = 'umap.new', raster = FALSE, label = T, ncol = 2)

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
db794c0	Gunjan Dixit	2024-06-13

Basal cells

Note: These markers are only specific to Basal

basal_markers <- c("KRT15", "KRT17", "TP63")
FeaturePlot(paed_sub,features=basal_markers, reduction = 'umap.new', raster = FALSE, label = T, ncol = 2)

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
db794c0	Gunjan Dixit	2024-06-13
e0e83af	Gunjan Dixit	2024-06-04

Azimuth Labels

## Finest level
DimPlot(paed_sub, reduction = "umap.new", group.by = "predicted.ann_finest_level", raster = FALSE, repel = TRUE, label = TRUE, label.size = 3.5) 

Past versions of unnamed-chunk-12-1.png

Version	Author	Date
db794c0	Gunjan Dixit	2024-06-13
e0e83af	Gunjan Dixit	2024-06-04

df_table <- as.data.frame(table(paed_sub$RNA_snn_res.0.1, paed_sub$predicted.ann_finest_level))
ggplot(df_table, aes(Var1, Freq, fill = Var2)) +
  geom_bar(stat = "identity") +
  labs(x = "RNA_snn_res.0.1", y = "Count", fill = "predicted ann_finest_level") +
  theme_minimal() +
  ggtitle("Stacked Bar Plot of basal/club/goblet (res=0.1) and predicted.ann_finest_level")

Past versions of unnamed-chunk-12-2.png

Version	Author	Date
db794c0	Gunjan Dixit	2024-06-13

## Predicted_Level 5
DimPlot(paed_sub, reduction = "umap.new", group.by = "predicted.ann_level_5", raster = FALSE, repel = TRUE, label = TRUE, label.size = 3.5) 

Past versions of unnamed-chunk-12-3.png

Version	Author	Date
db794c0	Gunjan Dixit	2024-06-13

df_table <- as.data.frame(table(paed_sub$RNA_snn_res.0.1, paed_sub$predicted.ann_level_5))

ggplot(df_table, aes(Var1, Freq, fill = Var2)) +
  geom_bar(stat = "identity") +
  labs(x = "RNA_snn_res.0.1", y = "Count", fill = "predicted ann_level_5") +
  theme_minimal() +
  ggtitle("Stacked Bar Plot of basal/club/goblet (res=0.1) and predicted.ann_level5")

Past versions of unnamed-chunk-12-4.png

Version	Author	Date
db794c0	Gunjan Dixit	2024-06-13

Reclustering T cell population

This includes CD4 T cell, CD8 T cell, NK cell, NK-T cell, proliferating or cycling T/NK cell.

The marker genes for this reclustering can be found here-

NB_Tcell_population_res.0.4

idx <- which(Idents(merged_obj) %in% c("CD4 T cells", "CD8 T cells", "NK-T cells", "proliferating T/NK"))
paed_sub <- merged_obj[,idx]
mito_genes <- grep("^MT-", rownames(paed_sub), value = TRUE)
paed_sub <- subset(paed_sub, features = setdiff(rownames(paed_sub), mito_genes))
paed_sub

An object of class Seurat 
17962 features across 21491 samples within 1 assay 
Active assay: RNA (17962 features, 2000 variable features)
 3 layers present: data, counts, scale.data
 4 dimensional reductions calculated: pca, umap.unintegrated, harmony, umap.harmony

paed_sub <- paed_sub %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() 
  
paed_sub <- RunUMAP(paed_sub, dims = 1:30, reduction = "pca", reduction.name = "umap.new")

meta_data_columns <- colnames(paed_sub@meta.data)
columns_to_remove <- grep("^RNA_snn_res", meta_data_columns, value = TRUE)
paed_sub@meta.data <- paed_sub@meta.data[, !(colnames(paed_sub@meta.data) %in% columns_to_remove)]


resolutions <- seq(0.1, 1, by = 0.1)
paed_sub <- FindNeighbors(paed_sub, reduction = "pca", dims = 1:30)
paed_sub <- FindClusters(paed_sub, resolution = resolutions, algorithm = 3)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21491
Number of edges: 703338

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9444
Number of communities: 6
Elapsed time: 16 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21491
Number of edges: 703338

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9168
Number of communities: 8
Elapsed time: 13 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21491
Number of edges: 703338

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9021
Number of communities: 11
Elapsed time: 12 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21491
Number of edges: 703338

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8894
Number of communities: 13
Elapsed time: 11 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21491
Number of edges: 703338

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8772
Number of communities: 14
Elapsed time: 11 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21491
Number of edges: 703338

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8676
Number of communities: 17
Elapsed time: 11 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21491
Number of edges: 703338

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8589
Number of communities: 17
Elapsed time: 10 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21491
Number of edges: 703338

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8509
Number of communities: 19
Elapsed time: 10 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21491
Number of edges: 703338

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8430
Number of communities: 20
Elapsed time: 10 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21491
Number of edges: 703338

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8360
Number of communities: 21
Elapsed time: 10 seconds

DimHeatmap(paed_sub, dims = 1:10, cells = 500, balanced = TRUE)

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
c20f60f	Gunjan Dixit	2024-07-08
db794c0	Gunjan Dixit	2024-06-13

clustree(paed_sub, prefix = "RNA_snn_res.")

Past versions of unnamed-chunk-15-1.png

Version	Author	Date
c20f60f	Gunjan Dixit	2024-07-08
db794c0	Gunjan Dixit	2024-06-13

opt_res <- "RNA_snn_res.0.4"  
n <- nlevels(paed_sub$RNA_snn_res.0.4)
paed_sub$RNA_snn_res.0.4 <- factor(paed_sub$RNA_snn_res.0.4, levels = seq(0,n-1))
paed_sub$seurat_clusters <- NULL
Idents(paed_sub) <- paed_sub$RNA_snn_res.0.4

DimPlot(paed_sub, reduction = "umap.new", group.by = "RNA_snn_res.0.4", label = TRUE, label.size = 4.5, repel = TRUE, raster = FALSE )

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
c20f60f	Gunjan Dixit	2024-07-08
db794c0	Gunjan Dixit	2024-06-13

paed_sub.markers <- FindAllMarkers(paed_sub, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

Calculating cluster 0

Calculating cluster 1

Calculating cluster 2

Calculating cluster 3

Calculating cluster 4

Calculating cluster 5

Calculating cluster 6

Calculating cluster 7

Calculating cluster 8

Calculating cluster 9

Calculating cluster 10

Calculating cluster 11

paed_sub.markers %>%
    group_by(cluster) %>% unique() %>% 
    top_n(n = 5, wt = avg_log2FC) -> top5

paed_sub.markers %>%
    group_by(cluster) %>% 
    slice_head(n=1) %>% 
    pull(gene) -> best.wilcox.gene.per.cluster

best.wilcox.gene.per.cluster

 [1] "CD8A"  "CSF1"  "CD4"   "TCF7"  "IFI6"  "MAF"   "NR4A2" "ITGAX" "TYMS" 
[10] "GZMK"  "KLF2"  "CD79A"

FeaturePlot(paed_sub,features=best.wilcox.gene.per.cluster, reduction = 'umap.new', raster = FALSE, label = T, ncol = 3)

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
c20f60f	Gunjan Dixit	2024-07-08

Top 10 marker genes from Seurat

## Seurat top markers
top10 <- paed_sub.markers %>%
    group_by(cluster) %>%
    top_n(n = 10, wt = avg_log2FC) %>%
    ungroup() %>%
    distinct(gene, .keep_all = TRUE) %>%
    arrange(cluster, desc(avg_log2FC))

cluster_colors <- paletteer::paletteer_d("pals::glasbey")[factor(top10$cluster)]

DotPlot(paed_sub,    
        features = unique(top10$gene),
        group.by = opt_res,
        cols = c("azure1", "blueviolet"),
        dot.scale = 3, assay = "RNA") +
    RotatedAxis() +
    FontSize(y.text = 8, x.text = 12) +
    labs(y = element_blank(), x = element_blank()) +
    coord_flip() +
    theme(axis.text.y = element_text(color = cluster_colors)) +
    ggtitle("Top 10 marker genes per cluster (Seurat)")

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

Past versions of unnamed-chunk-19-1.png

Version	Author	Date
c20f60f	Gunjan Dixit	2024-07-08
db794c0	Gunjan Dixit	2024-06-13

out_markers <- here("output",
            "CSV", 
            paste(tissue,"_Marker_genes_Reclustered_Tcell_population.",opt_res, sep = ""))

dir.create(out_markers, recursive = TRUE, showWarnings = FALSE)

for (cl in unique(paed_sub.markers$cluster)) {
  cluster_data <- paed_sub.markers %>% dplyr::filter(cluster == cl)
  file_name <- here(out_markers, paste0("G000231_Neeland_",tissue, "_cluster_", cl, ".csv"))
  write.csv(cluster_data, file = file_name)
}

Update T cell subclustering labels

cell_labels <- readxl::read_excel(here("data/Cell_labels_Mel_v2/earlyAIR_NB_BB_BAL_T-NK_annotations_16.07.24.xlsx"), sheet = "NB")
new_cluster_names <- cell_labels %>% 
  dplyr::select(cluster, annotation) %>% 
  deframe()

paed_sub <- RenameIdents(paed_sub, new_cluster_names)
paed_sub@meta.data$cell_labels_v2 <- Idents(paed_sub)

DimPlot(paed_sub, reduction = "umap.new", raster = FALSE, repel = TRUE, label = TRUE, label.size = 3.5) + ggtitle(paste0(tissue, ": UMAP with Updated subclustering"))

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
c20f60f	Gunjan Dixit	2024-07-08

Excluding contaminating labels

idx <- which(grepl("^contaminating", Idents(paed_sub)))
paed_clean <- paed_sub[, -idx]
DimPlot(paed_clean, reduction = "umap.new", raster = FALSE, repel = TRUE, label = TRUE, label.size = 3.5) + ggtitle(paste0(tissue, ":Updated subclustering (clean)"))

paed_clean <- paed_clean %>%
NormalizeData() %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA()

Normalizing layer: counts

Finding variable features for layer counts

Centering and scaling data matrix

Warning: Different features in new layer data than already exists for
scale.data

PC_ 1 
Positive:  TYMS, UHRF1, KIFC1, MKI67, RRM2, MYBL2, HIST1H1B, STMN1, ZWINT, TOP2A 
       TK1, BIRC5, FOXM1, HJURP, CDT1, CDK1, AURKB, E2F2, CDCA5, HIST1H2BH 
       ASF1B, SPC24, ESPL1, PKMYT1, TPX2, DLGAP5, ANLN, DTL, E2F1, NCAPG 
Negative:  TCF7, LTB, IL7R, TXNIP, CD300A, KLF2, PLAC8, PTGDR, LEF1, TXK 
       TSC22D3, SATB1, FOS, FCRL3, ITGAX, MAL, DTX1, FCMR, ITGAM, SORL1 
       RASGRP2, CHST2, CCR7, GAS7, CNR2, RASA3, SOCS3, BACH2, DUSP2, TRDC 
PC_ 2 
Positive:  CD300A, FCER1G, ITGAX, TYROBP, GNLY, PLAC8, SH2D1B, KLRF1, KLF2, FGR 
       IRF8, DUSP2, ITGAM, NCAM1, AREG, TCF7, FOSL2, TXK, GAS7, CTSW 
       SELL, PTGDR, FCGR3A, TNFRSF18, S1PR5, TRDC, FOS, SAMD3, NCR1, LITAF 
Negative:  CXCR6, CCR5, TRBC2, ALOX5AP, ITGA1, JUN, RORA, S100A4, CCR6, ST8SIA1 
       COL5A1, MAF, IL26, GZMA, TOX2, CD4, B3GALT2, CSF1, SCUBE1, CXCL13 
       LAG3, PDCD1, SLAMF1, ADAM19, RORC, CD5, CTSH, KLRB1, IL17A, NMUR1 
PC_ 3 
Positive:  NKG7, GZMA, HOPX, PRF1, GNLY, GZMB, KLRC1, KLRD1, ITGA1, KLRC4 
       ZNF683, NMUR1, PIK3AP1, FASLG, KLRC3, CTSW, CSF1, CLNK, IL2RB, CXCR6 
       FCRL6, KIR2DL4, ENTPD1, NCR1, GZMH, ATP8B4, CCL4, SCUBE1, ALOX5AP, ADGRG1 
Negative:  CD4, LTB, CD28, MAF, IL7R, SPOCK2, CD40LG, TCF7, CXCR5, IL6R 
       ICOS, CCR7, CD5, CCR4, CD27, KLF2, GPR183, SATB1, SELL, TBC1D4 
       TNFRSF25, FCMR, CTLA4, CXCR4, ACTN1, LEF1, SOCS3, RASGRP2, TNFRSF4, MAL 
PC_ 4 
Positive:  IFI6, LAG3, IFI44L, ISG15, GZMB, MX1, IFI44, OAS3, OAS1, TYMP 
       CMPK2, CXCR6, RSAD2, TNFAIP3, RGS1, IFIT1, XAF1, NR4A2, IRF7, PRDM1 
       FOSL2, ISG20, TNFRSF1B, CD69, SRGN, PRF1, ZFP36, USP18, TENT5C, SOCS1 
Negative:  TCF7, ITGAX, PTGDR, LEF1, CD300A, FCRL3, KLRC4, IGHM, ZNF683, ITGAM 
       TRDC, KLRC3, CNR2, FGR, FCRL6, TXK, GAS7, PLAC8, CXXC5, ID3 
       RIPOR2, MATK, SPRY2, KLRF1, MAL, SAMD3, ACTN1, TXNIP, DYSF, CHST2 
PC_ 5 
Positive:  EGR2, EGR3, NR4A1, NR4A3, NR4A2, ZFP36L1, TNFRSF9, EGR1, NFKBID, CRTAM 
       NAB2, TNFRSF18, KDM6B, CCL4L2, DUSP2, TIGIT, PHLDA1, ID3, CTLA4, IL21 
       XCL2, SRGN, CCL4, TOX2, KRT86, SPRY2, TNFRSF4, CCL3, ATP8B4, CSF1 
Negative:  IFI44L, IFI6, CMPK2, OAS3, ISG15, RSAD2, OAS1, IFIT1, MX1, XAF1 
       IFI44, MX2, LY6E, OAS2, USP18, GBP1, IFIT2, ISG20, IRF7, IFIT3 
       SAMD9L, KLF2, STAT1, GIMAP4, HERC5, MT2A, TMSB10, TYMP, RASGRP2, CX3CR1 

paed_clean <- RunUMAP(paed_clean, dims = 1:30, reduction = "pca", reduction.name = "umap.clean")

12:27:31 UMAP embedding parameters a = 0.9922 b = 1.112

Found more than one class "dist" in cache; using the first, from namespace 'spam'

Also defined by 'BiocGenerics'

12:27:31 Read 21032 rows and found 30 numeric columns

12:27:31 Using Annoy for neighbor search, n_neighbors = 30

Found more than one class "dist" in cache; using the first, from namespace 'spam'

Also defined by 'BiocGenerics'

12:27:31 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

[----|----|----|----|----|----|----|----|----|----|

**************************************************|
12:27:32 Writing NN index file to temp file /var/folders/q8/kw1r78g12qn793xm7g0zvk94x2bh70/T//Rtmp6JQpFt/file150670157b63
12:27:32 Searching Annoy index using 1 thread, search_k = 3000
12:27:36 Annoy recall = 100%
12:27:36 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
12:27:37 Initializing from normalized Laplacian + noise (using RSpectra)
12:27:38 Commencing optimization for 200 epochs, with 922708 positive edges
12:27:44 Optimization finished

DimPlot(paed_clean, reduction = "umap.clean", group.by = "cell_labels_v2",raster = FALSE, repel = TRUE, label = TRUE, label.size = 4.5) + ggtitle(paste0(tissue, ": Updated subclustering (clean)"))

Exploring Unknown cluster (cluster 17)

file <- here("output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c17.csv")

data <- read_csv(file) %>%
    arrange(PValue) %>%  
    slice_head(n = 30)   

cluster <- gsub(".*limma-c(\\d+)\\.csv", "Cluster \\1", basename(file))

p <- ggplot(data, aes(x = reorder(Pathway, -log10(PValue)), y = -log10(PValue), size = NGenes, color = Direction)) +
    geom_point(alpha = 0.7) +
    coord_flip() +
    scale_color_manual(values = c(Up = "green3", Down = "red3")) + 
    labs(title = cluster, x = NULL, y = "-log10(P-value)", size = "Number of Genes", color = "Direction") +
    theme_minimal() +
    theme(axis.text.y = element_text(size = 8), plot.title = element_text(hjust = 0.5))
  
p

Session Info

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.2 (2023-10-31)
 os       macOS Sonoma 14.5
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Australia/Melbourne
 date     2024-07-26
 pandoc   3.1.1 @ /Users/dixitgunjan/Desktop/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version    date (UTC) lib source
 abind                  1.4-5      2016-07-21 [1] CRAN (R 4.3.0)
 AnnotationDbi        * 1.64.1     2023-11-02 [1] Bioconductor
 backports              1.4.1      2021-12-13 [1] CRAN (R 4.3.0)
 beeswarm               0.4.0      2021-06-01 [1] CRAN (R 4.3.0)
 Biobase              * 2.62.0     2023-10-26 [1] Bioconductor
 BiocGenerics         * 0.48.1     2023-11-02 [1] Bioconductor
 BiocManager            1.30.22    2023-08-08 [1] CRAN (R 4.3.0)
 BiocStyle            * 2.30.0     2023-10-26 [1] Bioconductor
 Biostrings             2.70.2     2024-01-30 [1] Bioconductor 3.18 (R 4.3.2)
 bit                    4.0.5      2022-11-15 [1] CRAN (R 4.3.0)
 bit64                  4.0.5      2020-08-30 [1] CRAN (R 4.3.0)
 bitops                 1.0-7      2021-04-24 [1] CRAN (R 4.3.0)
 blob                   1.2.4      2023-03-17 [1] CRAN (R 4.3.0)
 bslib                  0.6.1      2023-11-28 [1] CRAN (R 4.3.1)
 cachem                 1.0.8      2023-05-01 [1] CRAN (R 4.3.0)
 callr                  3.7.5      2024-02-19 [1] CRAN (R 4.3.1)
 cellranger             1.1.0      2016-07-27 [1] CRAN (R 4.3.0)
 checkmate              2.3.1      2023-12-04 [1] CRAN (R 4.3.1)
 cli                    3.6.2      2023-12-11 [1] CRAN (R 4.3.1)
 cluster                2.1.6      2023-12-01 [1] CRAN (R 4.3.1)
 clustree             * 0.5.1      2023-11-05 [1] CRAN (R 4.3.1)
 codetools              0.2-19     2023-02-01 [1] CRAN (R 4.3.2)
 colorspace             2.1-0      2023-01-23 [1] CRAN (R 4.3.0)
 cowplot                1.1.3      2024-01-22 [1] CRAN (R 4.3.1)
 crayon                 1.5.2      2022-09-29 [1] CRAN (R 4.3.0)
 data.table           * 1.15.0     2024-01-30 [1] CRAN (R 4.3.1)
 DBI                    1.2.2      2024-02-16 [1] CRAN (R 4.3.1)
 DelayedArray           0.28.0     2023-11-06 [1] Bioconductor
 deldir                 2.0-2      2023-11-23 [1] CRAN (R 4.3.1)
 digest                 0.6.34     2024-01-11 [1] CRAN (R 4.3.1)
 dotCall64              1.1-1      2023-11-28 [1] CRAN (R 4.3.1)
 dplyr                * 1.1.4      2023-11-17 [1] CRAN (R 4.3.1)
 edgeR                * 4.0.16     2024-02-20 [1] Bioconductor 3.18 (R 4.3.2)
 ellipsis               0.3.2      2021-04-29 [1] CRAN (R 4.3.0)
 evaluate               0.23       2023-11-01 [1] CRAN (R 4.3.1)
 fansi                  1.0.6      2023-12-08 [1] CRAN (R 4.3.1)
 farver                 2.1.1      2022-07-06 [1] CRAN (R 4.3.0)
 fastDummies            1.7.3      2023-07-06 [1] CRAN (R 4.3.0)
 fastmap                1.1.1      2023-02-24 [1] CRAN (R 4.3.0)
 fitdistrplus           1.1-11     2023-04-25 [1] CRAN (R 4.3.0)
 forcats              * 1.0.0      2023-01-29 [1] CRAN (R 4.3.0)
 fs                     1.6.3      2023-07-20 [1] CRAN (R 4.3.0)
 future                 1.33.1     2023-12-22 [1] CRAN (R 4.3.1)
 future.apply           1.11.1     2023-12-21 [1] CRAN (R 4.3.1)
 generics               0.1.3      2022-07-05 [1] CRAN (R 4.3.0)
 GenomeInfoDb           1.38.6     2024-02-10 [1] Bioconductor 3.18 (R 4.3.2)
 GenomeInfoDbData       1.2.11     2024-02-27 [1] Bioconductor
 GenomicRanges          1.54.1     2023-10-30 [1] Bioconductor
 getPass                0.2-4      2023-12-10 [1] CRAN (R 4.3.1)
 ggbeeswarm             0.7.2      2023-04-29 [1] CRAN (R 4.3.0)
 ggforce                0.4.2      2024-02-19 [1] CRAN (R 4.3.1)
 ggplot2              * 3.5.0      2024-02-23 [1] CRAN (R 4.3.1)
 ggraph               * 2.1.0      2022-10-09 [1] CRAN (R 4.3.0)
 ggrastr                1.0.2      2023-06-01 [1] CRAN (R 4.3.0)
 ggrepel                0.9.5      2024-01-10 [1] CRAN (R 4.3.1)
 ggridges               0.5.6      2024-01-23 [1] CRAN (R 4.3.1)
 git2r                  0.33.0     2023-11-26 [1] CRAN (R 4.3.1)
 globals                0.16.2     2022-11-21 [1] CRAN (R 4.3.0)
 glue                 * 1.7.0      2024-01-09 [1] CRAN (R 4.3.1)
 goftest                1.2-3      2021-10-07 [1] CRAN (R 4.3.0)
 graphlayouts           1.1.0      2024-01-19 [1] CRAN (R 4.3.1)
 gridExtra              2.3        2017-09-09 [1] CRAN (R 4.3.0)
 gtable                 0.3.4      2023-08-21 [1] CRAN (R 4.3.0)
 here                 * 1.0.1      2020-12-13 [1] CRAN (R 4.3.0)
 highr                  0.10       2022-12-22 [1] CRAN (R 4.3.0)
 hms                    1.1.3      2023-03-21 [1] CRAN (R 4.3.0)
 htmltools              0.5.7      2023-11-03 [1] CRAN (R 4.3.1)
 htmlwidgets            1.6.4      2023-12-06 [1] CRAN (R 4.3.1)
 httpuv                 1.6.14     2024-01-26 [1] CRAN (R 4.3.1)
 httr                   1.4.7      2023-08-15 [1] CRAN (R 4.3.0)
 ica                    1.0-3      2022-07-08 [1] CRAN (R 4.3.0)
 igraph                 2.0.2      2024-02-17 [1] CRAN (R 4.3.1)
 IRanges              * 2.36.0     2023-10-26 [1] Bioconductor
 irlba                  2.3.5.1    2022-10-03 [1] CRAN (R 4.3.2)
 jquerylib              0.1.4      2021-04-26 [1] CRAN (R 4.3.0)
 jsonlite               1.8.8      2023-12-04 [1] CRAN (R 4.3.1)
 kableExtra           * 1.4.0      2024-01-24 [1] CRAN (R 4.3.1)
 KEGGREST               1.42.0     2023-10-26 [1] Bioconductor
 KernSmooth             2.23-22    2023-07-10 [1] CRAN (R 4.3.2)
 knitr                  1.45       2023-10-30 [1] CRAN (R 4.3.1)
 labeling               0.4.3      2023-08-29 [1] CRAN (R 4.3.0)
 later                  1.3.2      2023-12-06 [1] CRAN (R 4.3.1)
 lattice                0.22-5     2023-10-24 [1] CRAN (R 4.3.1)
 lazyeval               0.2.2      2019-03-15 [1] CRAN (R 4.3.0)
 leiden                 0.4.3.1    2023-11-17 [1] CRAN (R 4.3.1)
 lifecycle              1.0.4      2023-11-07 [1] CRAN (R 4.3.1)
 limma                * 3.58.1     2023-11-02 [1] Bioconductor
 listenv                0.9.1      2024-01-29 [1] CRAN (R 4.3.1)
 lmtest                 0.9-40     2022-03-21 [1] CRAN (R 4.3.0)
 locfit                 1.5-9.8    2023-06-11 [1] CRAN (R 4.3.0)
 lubridate            * 1.9.3      2023-09-27 [1] CRAN (R 4.3.1)
 magrittr               2.0.3      2022-03-30 [1] CRAN (R 4.3.0)
 MASS                   7.3-60.0.1 2024-01-13 [1] CRAN (R 4.3.1)
 Matrix                 1.6-5      2024-01-11 [1] CRAN (R 4.3.1)
 MatrixGenerics         1.14.0     2023-10-26 [1] Bioconductor
 matrixStats            1.2.0      2023-12-11 [1] CRAN (R 4.3.1)
 memoise                2.0.1      2021-11-26 [1] CRAN (R 4.3.0)
 mime                   0.12       2021-09-28 [1] CRAN (R 4.3.0)
 miniUI                 0.1.1.1    2018-05-18 [1] CRAN (R 4.3.0)
 munsell                0.5.0      2018-06-12 [1] CRAN (R 4.3.0)
 nlme                   3.1-164    2023-11-27 [1] CRAN (R 4.3.1)
 org.Hs.eg.db         * 3.18.0     2024-02-27 [1] Bioconductor
 paletteer              1.6.0      2024-01-21 [1] CRAN (R 4.3.1)
 parallelly             1.37.0     2024-02-14 [1] CRAN (R 4.3.1)
 patchwork            * 1.2.0      2024-01-08 [1] CRAN (R 4.3.1)
 pbapply                1.7-2      2023-06-27 [1] CRAN (R 4.3.0)
 pillar                 1.9.0      2023-03-22 [1] CRAN (R 4.3.0)
 pkgconfig              2.0.3      2019-09-22 [1] CRAN (R 4.3.0)
 plotly                 4.10.4     2024-01-13 [1] CRAN (R 4.3.1)
 plyr                   1.8.9      2023-10-02 [1] CRAN (R 4.3.1)
 png                    0.1-8      2022-11-29 [1] CRAN (R 4.3.0)
 polyclip               1.10-6     2023-09-27 [1] CRAN (R 4.3.1)
 presto                 1.0.0      2024-02-27 [1] Github (immunogenomics/presto@31dc97f)
 prismatic              1.1.1      2022-08-15 [1] CRAN (R 4.3.0)
 processx               3.8.3      2023-12-10 [1] CRAN (R 4.3.1)
 progressr              0.14.0     2023-08-10 [1] CRAN (R 4.3.0)
 promises               1.2.1      2023-08-10 [1] CRAN (R 4.3.0)
 ps                     1.7.6      2024-01-18 [1] CRAN (R 4.3.1)
 purrr                * 1.0.2      2023-08-10 [1] CRAN (R 4.3.0)
 R6                     2.5.1      2021-08-19 [1] CRAN (R 4.3.0)
 RANN                   2.6.1      2019-01-08 [1] CRAN (R 4.3.0)
 RColorBrewer         * 1.1-3      2022-04-03 [1] CRAN (R 4.3.0)
 Rcpp                   1.0.12     2024-01-09 [1] CRAN (R 4.3.1)
 RcppAnnoy              0.0.22     2024-01-23 [1] CRAN (R 4.3.1)
 RcppHNSW               0.6.0      2024-02-04 [1] CRAN (R 4.3.1)
 RCurl                  1.98-1.14  2024-01-09 [1] CRAN (R 4.3.1)
 readr                * 2.1.5      2024-01-10 [1] CRAN (R 4.3.1)
 readxl               * 1.4.3      2023-07-06 [1] CRAN (R 4.3.0)
 rematch2               2.1.2      2020-05-01 [1] CRAN (R 4.3.0)
 reshape2               1.4.4      2020-04-09 [1] CRAN (R 4.3.0)
 reticulate             1.35.0     2024-01-31 [1] CRAN (R 4.3.1)
 rlang                  1.1.3      2024-01-10 [1] CRAN (R 4.3.1)
 rmarkdown              2.25       2023-09-18 [1] CRAN (R 4.3.1)
 ROCR                   1.0-11     2020-05-02 [1] CRAN (R 4.3.0)
 rprojroot              2.0.4      2023-11-05 [1] CRAN (R 4.3.1)
 RSpectra               0.16-1     2022-04-24 [1] CRAN (R 4.3.0)
 RSQLite                2.3.5      2024-01-21 [1] CRAN (R 4.3.1)
 rstudioapi             0.15.0     2023-07-07 [1] CRAN (R 4.3.0)
 Rtsne                  0.17       2023-12-07 [1] CRAN (R 4.3.1)
 S4Arrays               1.2.0      2023-10-26 [1] Bioconductor
 S4Vectors            * 0.40.2     2023-11-25 [1] Bioconductor 3.18 (R 4.3.2)
 sass                   0.4.8      2023-12-06 [1] CRAN (R 4.3.1)
 scales                 1.3.0      2023-11-28 [1] CRAN (R 4.3.1)
 scattermore            1.2        2023-06-12 [1] CRAN (R 4.3.0)
 sctransform            0.4.1      2023-10-19 [1] CRAN (R 4.3.1)
 sessioninfo            1.2.2      2021-12-06 [1] CRAN (R 4.3.0)
 Seurat               * 5.0.1.9009 2024-02-28 [1] Github (satijalab/seurat@6a3ef5e)
 SeuratObject         * 5.0.1      2023-11-17 [1] CRAN (R 4.3.1)
 shiny                  1.8.0      2023-11-17 [1] CRAN (R 4.3.1)
 SingleCellExperiment   1.24.0     2023-11-06 [1] Bioconductor
 sp                   * 2.1-3      2024-01-30 [1] CRAN (R 4.3.1)
 spam                   2.10-0     2023-10-23 [1] CRAN (R 4.3.1)
 SparseArray            1.2.4      2024-02-10 [1] Bioconductor 3.18 (R 4.3.2)
 spatstat.data          3.0-4      2024-01-15 [1] CRAN (R 4.3.1)
 spatstat.explore       3.2-6      2024-02-01 [1] CRAN (R 4.3.1)
 spatstat.geom          3.2-8      2024-01-26 [1] CRAN (R 4.3.1)
 spatstat.random        3.2-2      2023-11-29 [1] CRAN (R 4.3.1)
 spatstat.sparse        3.0-3      2023-10-24 [1] CRAN (R 4.3.1)
 spatstat.utils         3.0-4      2023-10-24 [1] CRAN (R 4.3.1)
 speckle              * 1.2.0      2023-10-26 [1] Bioconductor
 statmod                1.5.0      2023-01-06 [1] CRAN (R 4.3.0)
 stringi                1.8.3      2023-12-11 [1] CRAN (R 4.3.1)
 stringr              * 1.5.1      2023-11-14 [1] CRAN (R 4.3.1)
 SummarizedExperiment   1.32.0     2023-11-06 [1] Bioconductor
 survival               3.5-8      2024-02-14 [1] CRAN (R 4.3.1)
 svglite                2.1.3      2023-12-08 [1] CRAN (R 4.3.1)
 systemfonts            1.0.5      2023-10-09 [1] CRAN (R 4.3.1)
 tensor                 1.5        2012-05-05 [1] CRAN (R 4.3.0)
 tibble               * 3.2.1      2023-03-20 [1] CRAN (R 4.3.0)
 tidygraph              1.3.1      2024-01-30 [1] CRAN (R 4.3.1)
 tidyr                * 1.3.1      2024-01-24 [1] CRAN (R 4.3.1)
 tidyselect             1.2.0      2022-10-10 [1] CRAN (R 4.3.0)
 tidyverse            * 2.0.0      2023-02-22 [1] CRAN (R 4.3.0)
 timechange             0.3.0      2024-01-18 [1] CRAN (R 4.3.1)
 tweenr                 2.0.3      2024-02-26 [1] CRAN (R 4.3.1)
 tzdb                   0.4.0      2023-05-12 [1] CRAN (R 4.3.0)
 utf8                   1.2.4      2023-10-22 [1] CRAN (R 4.3.1)
 uwot                   0.1.16     2023-06-29 [1] CRAN (R 4.3.0)
 vctrs                  0.6.5      2023-12-01 [1] CRAN (R 4.3.1)
 vipor                  0.4.7      2023-12-18 [1] CRAN (R 4.3.1)
 viridis                0.6.5      2024-01-29 [1] CRAN (R 4.3.1)
 viridisLite            0.4.2      2023-05-02 [1] CRAN (R 4.3.0)
 vroom                  1.6.5      2023-12-05 [1] CRAN (R 4.3.1)
 whisker                0.4.1      2022-12-05 [1] CRAN (R 4.3.0)
 withr                  3.0.0      2024-01-16 [1] CRAN (R 4.3.1)
 workflowr            * 1.7.1      2023-08-23 [1] CRAN (R 4.3.0)
 xfun                   0.42       2024-02-08 [1] CRAN (R 4.3.1)
 xml2                   1.3.6      2023-12-04 [1] CRAN (R 4.3.1)
 xtable                 1.8-4      2019-04-21 [1] CRAN (R 4.3.0)
 XVector                0.42.0     2023-10-26 [1] Bioconductor
 yaml                   2.3.8      2023-12-11 [1] CRAN (R 4.3.1)
 zlibbioc               1.48.0     2023-10-26 [1] Bioconductor
 zoo                    1.8-12     2023-04-13 [1] CRAN (R 4.3.0)

 [1] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library

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

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

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

time zone: Australia/Melbourne
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] readxl_1.4.3         org.Hs.eg.db_3.18.0  AnnotationDbi_1.64.1
 [4] IRanges_2.36.0       S4Vectors_0.40.2     Biobase_2.62.0      
 [7] BiocGenerics_0.48.1  speckle_1.2.0        edgeR_4.0.16        
[10] limma_3.58.1         patchwork_1.2.0      data.table_1.15.0   
[13] RColorBrewer_1.1-3   kableExtra_1.4.0     clustree_0.5.1      
[16] ggraph_2.1.0         Seurat_5.0.1.9009    SeuratObject_5.0.1  
[19] sp_2.1-3             glue_1.7.0           here_1.0.1          
[22] lubridate_1.9.3      forcats_1.0.0        stringr_1.5.1       
[25] dplyr_1.1.4          purrr_1.0.2          readr_2.1.5         
[28] tidyr_1.3.1          tibble_3.2.1         ggplot2_3.5.0       
[31] tidyverse_2.0.0      BiocStyle_2.30.0     workflowr_1.7.1     

loaded via a namespace (and not attached):
  [1] fs_1.6.3                    matrixStats_1.2.0          
  [3] spatstat.sparse_3.0-3       bitops_1.0-7               
  [5] httr_1.4.7                  tools_4.3.2                
  [7] sctransform_0.4.1           backports_1.4.1            
  [9] utf8_1.2.4                  R6_2.5.1                   
 [11] lazyeval_0.2.2              uwot_0.1.16                
 [13] withr_3.0.0                 gridExtra_2.3              
 [15] progressr_0.14.0            cli_3.6.2                  
 [17] spatstat.explore_3.2-6      fastDummies_1.7.3          
 [19] prismatic_1.1.1             labeling_0.4.3             
 [21] sass_0.4.8                  spatstat.data_3.0-4        
 [23] ggridges_0.5.6              pbapply_1.7-2              
 [25] systemfonts_1.0.5           svglite_2.1.3              
 [27] sessioninfo_1.2.2           parallelly_1.37.0          
 [29] rstudioapi_0.15.0           RSQLite_2.3.5              
 [31] generics_0.1.3              vroom_1.6.5                
 [33] ica_1.0-3                   spatstat.random_3.2-2      
 [35] Matrix_1.6-5                ggbeeswarm_0.7.2           
 [37] fansi_1.0.6                 abind_1.4-5                
 [39] lifecycle_1.0.4             whisker_0.4.1              
 [41] yaml_2.3.8                  SummarizedExperiment_1.32.0
 [43] SparseArray_1.2.4           Rtsne_0.17                 
 [45] paletteer_1.6.0             grid_4.3.2                 
 [47] blob_1.2.4                  promises_1.2.1             
 [49] crayon_1.5.2                miniUI_0.1.1.1             
 [51] lattice_0.22-5              cowplot_1.1.3              
 [53] KEGGREST_1.42.0             pillar_1.9.0               
 [55] knitr_1.45                  GenomicRanges_1.54.1       
 [57] future.apply_1.11.1         codetools_0.2-19           
 [59] leiden_0.4.3.1              getPass_0.2-4              
 [61] vctrs_0.6.5                 png_0.1-8                  
 [63] spam_2.10-0                 cellranger_1.1.0           
 [65] gtable_0.3.4                rematch2_2.1.2             
 [67] cachem_1.0.8                xfun_0.42                  
 [69] S4Arrays_1.2.0              mime_0.12                  
 [71] tidygraph_1.3.1             survival_3.5-8             
 [73] SingleCellExperiment_1.24.0 statmod_1.5.0              
 [75] ellipsis_0.3.2              fitdistrplus_1.1-11        
 [77] ROCR_1.0-11                 nlme_3.1-164               
 [79] bit64_4.0.5                 RcppAnnoy_0.0.22           
 [81] GenomeInfoDb_1.38.6         rprojroot_2.0.4            
 [83] bslib_0.6.1                 irlba_2.3.5.1              
 [85] vipor_0.4.7                 KernSmooth_2.23-22         
 [87] colorspace_2.1-0            DBI_1.2.2                  
 [89] ggrastr_1.0.2               tidyselect_1.2.0           
 [91] processx_3.8.3              bit_4.0.5                  
 [93] compiler_4.3.2              git2r_0.33.0               
 [95] xml2_1.3.6                  DelayedArray_0.28.0        
 [97] plotly_4.10.4               checkmate_2.3.1            
 [99] scales_1.3.0                lmtest_0.9-40              
[101] callr_3.7.5                 digest_0.6.34              
[103] goftest_1.2-3               spatstat.utils_3.0-4       
[105] presto_1.0.0                rmarkdown_2.25             
[107] XVector_0.42.0              htmltools_0.5.7            
[109] pkgconfig_2.0.3             MatrixGenerics_1.14.0      
[111] highr_0.10                  fastmap_1.1.1              
[113] rlang_1.1.3                 htmlwidgets_1.6.4          
[115] shiny_1.8.0                 farver_2.1.1               
[117] jquerylib_0.1.4             zoo_1.8-12                 
[119] jsonlite_1.8.8              RCurl_1.98-1.14            
[121] magrittr_2.0.3              GenomeInfoDbData_1.2.11    
[123] dotCall64_1.1-1             munsell_0.5.0              
[125] Rcpp_1.0.12                 viridis_0.6.5              
[127] reticulate_1.35.0           stringi_1.8.3              
[129] zlibbioc_1.48.0             MASS_7.3-60.0.1            
[131] plyr_1.8.9                  parallel_4.3.2             
[133] listenv_0.9.1               ggrepel_0.9.5              
[135] deldir_2.0-2                Biostrings_2.70.2          
[137] graphlayouts_1.1.0          splines_4.3.2              
[139] tensor_1.5                  hms_1.1.3                  
[141] locfit_1.5-9.8              ps_1.7.6                   
[143] igraph_2.0.2                spatstat.geom_3.2-8        
[145] RcppHNSW_0.6.0              reshape2_1.4.4             
[147] evaluate_0.23               BiocManager_1.30.22        
[149] tzdb_0.4.0                  tweenr_2.0.3               
[151] httpuv_1.6.14               RANN_2.6.1                 
[153] polyclip_1.10-6             future_1.33.1              
[155] scattermore_1.2             ggforce_0.4.2              
[157] xtable_1.8-4                RSpectra_0.16-1            
[159] later_1.3.2                 viridisLite_0.4.2          
[161] beeswarm_0.4.0              memoise_2.0.1              
[163] cluster_2.1.6               timechange_0.3.0           
[165] globals_0.16.2