Subclustering: Nasal_brushings

Unsupervised Clustering of Broad cell labels

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Last updated: 2024-09-26

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Knit directory: paed-airway-allTissues/

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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Subclustering_Nasal_brushings.Rmd) and HTML (docs/Subclustering_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
html	952acbb	Gunjan Dixit	2024-09-26	Added B cell subclustering html
Rmd	77b017d	Gunjan Dixit	2024-09-26	Added B cell subclustering
html	07af966	Gunjan Dixit	2024-09-25	Modified index
Rmd	3b5ab22	Gunjan Dixit	2024-09-25	Separated Subclustering Rmd

---

Introduction

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

merged_obj <- readRDS(out1)
merged_obj

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

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: 15 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-2-1.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

clustree(paed_sub, prefix = "RNA_snn_res.")

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

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-5-1.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

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-6-1.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

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-7-1.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

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"))
  if (!file.exists(file_name)) {
  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-9-1.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

paed_sub@meta.data %>%
  ggplot(aes(x = cell_labels_v2, fill = cell_labels_v2)) +
  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"))

Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(count)` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

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

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

Additional QC plots for Proliferatink T/NK cluster

FeaturePlot(merged_obj, features=c("CD19", "MKI67"), reduction = 'umap.harmony', raster = FALSE, label = T, ncol = 2)

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

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

DimPlot(merged_obj, reduction = "umap.harmony", group.by = "predicted.ann_level_4", label = TRUE)

Past versions of unnamed-chunk-11-2.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

cl_16 <- as.data.frame(table(merged_obj$predicted.ann_level_4[merged_obj$RNA_snn_res.0.4 == "16"]))

colnames(cl_16) <- c("CellType", "Count")
cl_16 <- cl_16 %>% filter(Count > 0)
ggplot(cl_16, aes(x = reorder(CellType, Count), y = Count, fill = CellType)) +
geom_bar(stat = "identity") +
geom_text(aes(label = Count), hjust = -0.3) +  
coord_flip() +  
labs(title = "Azimuth (level4) cell types in Cluster 16 or Proliferating T/NK cells", x = "Cell Type", y = "Count") +
theme_minimal() +
theme(legend.position = "none")  

Past versions of unnamed-chunk-11-3.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

#FeaturePlot(merged_obj, features = c('nCount_RNA',"nFeature_RNA"), pt.size=0.2, label = T, ncol =2)

#VlnPlot(merged_obj,features = c("nCount_RNA","nFeature_RNA"), pt.size = 0) &
#theme(plot.title = element_text(size=10))

#VlnPlot(merged_obj,features = c("sum","detected"), pt.size = 0) &
#theme(plot.title = element_text(size=10))

Idents(merged_obj) <- merged_obj$cluster
cl16_cells <- WhichCells(merged_obj, idents = 16)
Idents(merged_obj) <- merged_obj$predicted.ann_level_4
cl_azimuth_proliferating_Tcells <- WhichCells(merged_obj, idents = "T cells proliferating")
DimPlot(paed_sub, reduction = "umap.new", group.by = "RNA_snn_res.0.4", label = TRUE, label.size = 4.5, repel = TRUE, raster = FALSE , cells.highlight = cl16_cells)

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

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

DimPlot(paed_sub, reduction = "umap.new", label = TRUE, label.size = 4.5, repel = TRUE, raster = FALSE , cells.highlight = cl_azimuth_proliferating_Tcells)

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

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

DimPlot(paed_sub, reduction = "umap.new", group.by = "predicted.ann_level_4", label = TRUE, label.size = 4.5, repel = TRUE, raster = FALSE)

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

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

Save subclustered SEU object

out2 <- here("output",
            "RDS", "AllBatches_Subclustering_SEUs", tissue,
             paste0("G000231_Neeland_",tissue,".Tcell_population.subclusters.SEU.rds"))
#dir.create(out2)
if (!file.exists(out2)) {
  saveRDS(paed_sub, file = out2)
}

Excluding contaminating labels

idx <- which(grepl("^contaminating", Idents(paed_sub)))
paed_clean <- paed_sub[, -idx]

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")

10:24:22 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'

10:24:22 Read 21032 rows and found 30 numeric columns

10:24:22 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'

10:24:22 Building Annoy index with metric = cosine, n_trees = 50

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

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

**************************************************|
10:24:23 Writing NN index file to temp file /var/folders/q8/kw1r78g12qn793xm7g0zvk94x2bh70/T//RtmpewC7Yy/file5ed82829d386
10:24:23 Searching Annoy index using 1 thread, search_k = 3000
10:24:26 Annoy recall = 100%
10:24:27 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
10:24:28 Initializing from normalized Laplacian + noise (using RSpectra)
10:24:28 Commencing optimization for 200 epochs, with 922708 positive edges
10:24:34 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)"))

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

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

Summary Plots

palette1 <- paletteer::paletteer_d("ggthemes::Classic_20")
palette2 <- paletteer::paletteer_d("Polychrome::light")
combined_palette <- unique(c(palette1, palette2))


p1 <- paed_sub@meta.data %>%
     dplyr::select(!!sym(opt_res), cell_labels_v2) %>% ggplot(aes(x = !!sym(opt_res), 
                                                                  fill = cell_labels_v2)) +
     geom_bar() +
     geom_text(aes(label = ..count..), stat = "count",
               vjust = -0.5, colour = "black", size = 2) +
     scale_y_log10() +
     theme(axis.text.x = element_blank(),
           axis.title.x = element_blank(),
           axis.ticks.x = element_blank()) +
     labs(y = "No. Cells (log scale)")

p2 <- paed_sub@meta.data %>%
  dplyr::select(!!sym(opt_res), Sample) %>%
  group_by(!!sym(opt_res), Sample) %>%
  summarise(num = n()) %>%
  mutate(prop = num / sum(num)) %>%
  ggplot(aes(x = !!sym(opt_res), y = prop * 100, 
             fill = Sample)) + 
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, 
                                   vjust = 0.5, 
                                   hjust = 1,
                                   size = 8)) +
  labs(y = "% Cells", fill = "Sample") +
  scale_fill_manual(values = combined_palette)

`summarise()` has grouped output by 'RNA_snn_res.0.4'. You can override using
the `.groups` argument.

# Combine the plots
(p1 / p2) & theme( legend.text = element_text(size = 8),
                            legend.key.size = unit(3, "mm"))

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

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

Other Clusters (excluding subclusters)

idx <- which(Idents(merged_obj) %in% c("CD4 T cells", "CD8 T cells", "NK-T cells", "proliferating T/NK"))
paed_other <- merged_obj[,-idx]
paed_other

An object of class Seurat 
17973 features across 54615 samples within 1 assay 
Active assay: RNA (17973 features, 2000 variable features)
 3 layers present: data, counts, scale.data
 4 dimensional reductions calculated: pca, umap.unintegrated, harmony, umap.harmony

levels(paed_other$cell_labels)[levels(paed_other$cell_labels) == "goblet/club/basal cells"] <- "non-ciliated cells"
levels(Idents(paed_other))[levels(Idents(paed_other)) == "goblet/club/basal cells"] <- "non-ciliated cells"
paed_other$cell_labels_v2 <- Idents(paed_other)

# Visualize the clustering results
DimPlot(paed_other, reduction = "umap.harmony", group.by = "cluster", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

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

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

DimPlot(paed_other, reduction = "umap.harmony", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE ) +NoLegend()

Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Past versions of unnamed-chunk-18-2.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

Save subclustered SEU object ( All other cells)

out2 <- here("output",
            "RDS", "AllBatches_Subclustering_SEUs", tissue,
             paste0("G000231_Neeland_",tissue,".all_other.subclusters.SEU.rds"))
#dir.create(out2)
if (!file.exists(out2)) {
  saveRDS(paed_other, file = out2)
}

Merge seurat objects of subclusters

files <- list.files(here("output",
            "RDS", "AllBatches_Subclustering_SEUs", tissue),
                    full.names = TRUE)

seu_list <- lapply(files, function(f) readRDS(f))

seu <- merge(seu_list[[1]], 
             y = seu_list[[2]])
seu

An object of class Seurat 
17973 features across 74812 samples within 1 assay 
Active assay: RNA (17973 features, 2000 variable features)
 6 layers present: data.1, data.2, counts.1, scale.data.1, counts.2, scale.data.2

Exploring the contaminated B cell cluster in T cells

idx <- which(Idents(seu) %in% c("B cells", "plasma B cells", "contaminating  B cell"))
paed_bcells <- seu[,idx]


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

Normalizing layer: counts.1

Normalizing layer: counts.2

Finding variable features for layer counts.1

Finding variable features for layer counts.2

Centering and scaling data matrix

PC_ 1 
Positive:  MEF2B, MKI67, RGS13, MYBL1, NUGGC, TOP2A, CDK1, TYMS, HMCES, DCAF12 
       CCNB2, UHRF1, AFF2, HJURP, HIST1H1B, MYBL2, CCDC88A, MME, BCL6, SEMA4A 
       CDC20, SPRED2, S1PR2, DEPDC1B, ASPM, CCNA2, SPC25, SYNE2, LOXL2, RAPGEF5 
Negative:  FCMR, B2M, TXNIP, KLF2, CD44, MPEG1, PARP15, SELL, PLAC8, IGHM 
       CCR6, FGD2, TNFRSF13B, PHACTR1, TRIM22, CAPG, EMP3, ZEB2, LY6E, CELF2 
       PLEKHO1, SPOCK2, TSC22D3, LIMD1, IGHD, COL19A1, P2RY10, RIPOR1, JUN, RIN3 
PC_ 2 
Positive:  TCL1A, IGHM, IGHD, NIBAN3, MARCKSL1, DTX1, CD72, HMCES, BACH2, NEIL1 
       BCL7A, MEF2B, ELL3, MMP17, BCL6, MYBL1, NUGGC, MME, SEMA4A, CD200 
       LOXL2, RAPGEF5, VPREB3, KLHL14, SLC30A4, AFF2, H1FX, SPRED2, FCMR, SELL 
Negative:  ITGAX, FCRL4, KCTD12, TNFRSF13B, IFI30, CCR1, IL2RB, GSN, GRN, SIGLEC6 
       TESC, SEMA7A, BHLHE40, CCR5, VIM, BHLHE41, ITGB7, CD82, CTSH, PREX1 
       SOX5, IGHA1, THEMIS2, DUSP4, CAPG, HSPA6, TYMP, CCDC50, HCK, EFHD2 
PC_ 3 
Positive:  MEF2B, LOXL2, SEMA4A, LMO2, RGS13, RAPGEF5, CAMK1, SPRED2, SERPINA9, HMCES 
       NEIL1, PTPRS, KLHL6, NUGGC, AFF2, EML6, BCL6, FGD6, SYNE2, MYO1E 
       ELL3, CCDC88A, S1PR2, LPP, HTR3A, P2RY12, MAML3, MME, CTSH, PLXNB2 
Negative:  TOP2A, MKI67, HIST1H1B, HJURP, CDK1, CDC20, CCNB2, TYMS, CCNA2, DLGAP5 
       HIST1H2BH, KIF23, DEPDC1B, ASPM, PLK1, SPC25, HMMR, AURKA, ANLN, CENPF 
       CENPE, KIF18B, TROAP, NEK2, HIST1H2BB, CDKN3, PBK, UBE2C, DEPDC1, CCNB1 
PC_ 4 
Positive:  FCRL4, PLK1, TOP2A, HJURP, MKI67, ASPM, CDC20, KIF23, CCNB2, KCTD12 
       CDK1, HMMR, DLGAP5, CENPF, TROAP, IGHA2, DEPDC1B, AURKA, ANLN, IGHA1 
       KIF18B, UBE2C, KIF14, CCR5, KIF20A, CENPE, CCR1, GSN, CCNB1, ITGAX 
Negative:  CD83, EGR3, NR4A1, HSP90AB1, NFKBIA, CD72, KDM6B, DUSP2, CD69, NFKBID 
       ISG20, IGHM, JUNB, IL21R, NFKB2, IGHD, MYC, IFI44L, SOCS3, SRM 
       TRAF4, SYNGR2, SOD2, DDX21, NOP16, FOS, IFI44, G0S2, BIRC3, IFI6 
PC_ 5 
Positive:  RESF1, CD48, B2M, DOCK10, CD24, DDX18, CLECL1, SAMSN1, CCR6, HNRNPU 
       PRDX1, RABGGTB, ODC1, IGHA2, GALNT1, SET, PARP15, CDV3, ZNF146, TRBC2 
       SESN3, MICOS10, TRAM1, CHCHD2, SPCS3, WDR3, CANX, MGAT4A, EIF3M, SPCS1 
Negative:  G0S2, CSF3R, PLAUR, FOSL2, CXCL8, FOS, DUSP1, AQP9, SOD2, NR4A1 
       NR4A2, ZFP36, IL1B, OSM, SOCS3, PPP1R15A, JUNB, NR4A3, FFAR2, ITGAX 
       IL1RN, FOSB, SLC11A1, PLK1, TSC22D3, KDM6B, HCAR3, TREM1, ISG20, NAMPT 

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

10:25:21 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'

10:25:21 Read 11412 rows and found 30 numeric columns

10:25:21 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'

10:25:21 Building Annoy index with metric = cosine, n_trees = 50

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

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

**************************************************|
10:25:22 Writing NN index file to temp file /var/folders/q8/kw1r78g12qn793xm7g0zvk94x2bh70/T//RtmpewC7Yy/file5ed828118d09
10:25:22 Searching Annoy index using 1 thread, search_k = 3000
10:25:24 Annoy recall = 100%
10:25:24 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
10:25:25 Initializing from normalized Laplacian + noise (using RSpectra)
10:25:25 Commencing optimization for 200 epochs, with 500484 positive edges
10:25:29 Optimization finished

DimPlot(paed_bcells, group.by = "cell_labels_v2",raster = FALSE, repel = TRUE, label = TRUE, label.size = 4.5)

Past versions of unnamed-chunk-20-1.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

DimPlot(paed_bcells,raster = FALSE, repel = TRUE, label = TRUE, label.size = 4.5)

Past versions of unnamed-chunk-20-2.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

Reclustering B cells

The marker genes for this reclustering can be found here-

NB_Bcell_population_res.0.2

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


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

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

Number of nodes: 11412
Number of edges: 378368

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9359
Number of communities: 6
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11412
Number of edges: 378368

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9066
Number of communities: 9
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11412
Number of edges: 378368

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8899
Number of communities: 10
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11412
Number of edges: 378368

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8732
Number of communities: 10
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11412
Number of edges: 378368

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8569
Number of communities: 11
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11412
Number of edges: 378368

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8430
Number of communities: 12
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11412
Number of edges: 378368

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8295
Number of communities: 12
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11412
Number of edges: 378368

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8162
Number of communities: 13
Elapsed time: 4 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11412
Number of edges: 378368

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8051
Number of communities: 14
Elapsed time: 4 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 11412
Number of edges: 378368

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.7943
Number of communities: 15
Elapsed time: 4 seconds

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

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

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

clustree(paed_bcells, prefix = "RNA_snn_res.")

Past versions of unnamed-chunk-22-1.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

opt_res <- "RNA_snn_res.0.2"  
n <- nlevels(paed_bcells$RNA_snn_res.0.2)
paed_bcells$RNA_snn_res.0.2 <- factor(paed_bcells$RNA_snn_res.0.2, levels = seq(0,n-1))
paed_bcells$seurat_clusters <- NULL
Idents(paed_bcells) <- paed_bcells$RNA_snn_res.0.2

DimPlot(paed_bcells, group.by = "RNA_snn_res.0.2", label = TRUE, label.size = 4.5, repel = TRUE, raster = FALSE )

Past versions of unnamed-chunk-24-1.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

paed_bcells <- JoinLayers(paed_bcells)
paed_bcells.markers <- FindAllMarkers(paed_bcells, 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

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

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

best.wilcox.gene.per.cluster

[1] "B2M"   "IGHD"  "DUSP1" "ITGAX" "MEF2B" "HMGB2" "G0S2"  "MZB1"  "CD2"  

FeaturePlot(paed_bcells,features=best.wilcox.gene.per.cluster, raster = FALSE, label = T, ncol = 3)

Past versions of unnamed-chunk-25-1.png

Version	Author	Date
07af966	Gunjan Dixit	2024-09-25

Top 10 marker genes from Seurat

## Seurat top markers
top10 <- paed_bcells.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_bcells,    
        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-26-1.png

Version	Author	Date
952acbb	Gunjan Dixit	2024-09-26

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

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

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

Excluding contaminating labels

idx <- which(grepl("^contaminating", Idents(seu)))
seu <- seu[, -idx]

merged <- seu %>%
NormalizeData() %>%
FindVariableFeatures() %>%
ScaleData() %>%
RunPCA()

Normalizing layer: counts.1

Normalizing layer: counts.2

Finding variable features for layer counts.1

Finding variable features for layer counts.2

Centering and scaling data matrix

PC_ 1 
Positive:  PIFO, ROPN1L, SNTN, RSPH4A, EFCAB1, CAPSL, FAM183A, SPA17, DNAH9, MORN2 
       C1orf194, CFAP126, CCDC65, ARMC3, C9orf135, FAM81B, ERICH3, CC2D2A, RSPH9, FAM216B 
       AKAP14, DRC1, AK7, NPHP1, LRRC23, CCN2, CCDC113, AGR3, CFAP206, MORN5 
Negative:  LCP1, ACTB, SRGN, TRBC2, CXCR4, COTL1, IL32, CCL5, LCP2, CD7 
       IL2RB, CNN2, CD2, LTB, CD8A, ITGB2, NKG7, ALOX5AP, VIM, TNFRSF1B 
       EMP3, CXCR6, SERPINB9, ZEB2, IRF8, FCMR, PRF1, TAGAP, CD79A, AOAH 
PC_ 2 
Positive:  LCP1, VIM, TRBC2, CCL5, SRGN, COTL1, IL2RB, CD2, CD8A, CXCR4 
       LTB, CD7, NKG7, IL32, LCP2, CXCR6, ALOX5AP, PRF1, GZMA, FCMR 
       TRBC1, CD79A, ACTB, CCR5, CD69, ITGB2, AOAH, IGHM, IRF8, JAML 
Negative:  TACSTD2, SPINT1, MUC1, ASS1, SDC1, JUP, CAPN13, SCNN1A, TRIM29, PRSS8 
       ELF3, CLDN7, CEACAM5, ERN2, CDH3, ST14, PI3, ID1, MUC4, HS3ST1 
       CNN3, CLDN4, KLK11, CYP2F1, ARHGAP23, KIAA1522, PIGR, MYH14, BPIFA1, IDO1 
PC_ 3 
Positive:  CCL5, TRBC2, CD8A, CD2, IL32, IL2RB, CXCR6, CD7, GZMA, TRBC1 
       ZNF683, PRF1, NKG7, KLRC4, HOPX, TIGIT, KLRD1, CTSW, FASLG, FCRL6 
       KLRC3, ITM2C, CD5, GNLY, FCMR, KLRC1, CST7, TCF7, GZMH, LDHB 
Negative:  LILRB2, CSF1R, FCER1G, CD14, CD163, SERPINA1, EMILIN2, TYROBP, AIF1, SLC8A1 
       LILRA5, MS4A6A, CD68, MAFB, FPR1, ADGRE2, STAB1, SPI1, TGFBI, CSF3R 
       PLAUR, APOBEC3A, MEFV, FCGR3A, TMEM176B, CSF2RA, VASH1, CTSL, CALHM6, ENPP2 
PC_ 4 
Positive:  CEACAM5, CEACAM6, SLC26A4, MLPH, SPNS2, PI3, STEAP4, CYP2F1, FCGBP, TSPAN8 
       PIGR, ALPL, MSMB, ATP10B, TFF3, BPIFA1, SLURP2, TFF1, GDF15, CES1 
       DUOXA2, ERN2, DUOX2, BPIFB1, SDCBP2, RARRES1, SYT7, ECRG4, SLC9A3, IGFBP7 
Negative:  ANLN, CDC20, CDK1, TOP2A, KIFC1, MKI67, FOXM1, ZWINT, TYMS, CCNB2 
       DLGAP5, RRM2, KIF23, ASF1B, TPX2, BIRC5, CIT, TK1, KIF20A, NEK2 
       SPC24, E2F7, BUB1, HJURP, POC1A, STMN1, CDCA5, CENPF, HIST1H1B, KIF2C 
PC_ 5 
Positive:  CD79A, IGHM, IGKC, IRF8, IGHD, BCL11A, POU2AF1, FCRL5, SPIB, BLK 
       MPEG1, CLEC17A, TCL1A, TNFRSF13B, CXCR5, RASGRP2, CR1, CYBB, CD83, IGHG1 
       LTB, CR2, PLCG2, AFF3, LRMP, FCMR, CTSH, CCR6, IGHA1, SELL 
Negative:  CCL5, IL32, CD7, NKG7, CD8A, IL2RB, PRF1, CXCR6, CD2, GZMA 
       LAG3, LCP2, ZNF683, KLRD1, KLRC4, GNLY, GZMB, CST7, CTSW, AOAH 
       FASLG, TRBC1, SRGN, CCR5, CSF1, JAML, TRBC2, HOPX, KLRC3, TIGIT 

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

10:27:32 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'

10:27:32 Read 74353 rows and found 30 numeric columns

10:27:32 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'

10:27:32 Building Annoy index with metric = cosine, n_trees = 50

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

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

**************************************************|
10:27:36 Writing NN index file to temp file /var/folders/q8/kw1r78g12qn793xm7g0zvk94x2bh70/T//RtmpewC7Yy/file5ed81d4ce01e
10:27:36 Searching Annoy index using 1 thread, search_k = 3000
10:27:50 Annoy recall = 100%
10:27:50 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
10:27:52 Initializing from normalized Laplacian + noise (using RSpectra)
10:28:06 Commencing optimization for 200 epochs, with 3225212 positive edges
10:28:28 Optimization finished

p4 <- DimPlot(merged, reduction = "umap.merged", group.by = "cell_labels_v2",raster = FALSE, repel = TRUE, label = TRUE, label.size = 4.5) + ggtitle(paste0(tissue, ": UMAP with annotations")) + NoLegend()
p4

Past versions of unnamed-chunk-29-1.png

Version	Author	Date
952acbb	Gunjan Dixit	2024-09-26

Save Final SEU object (All cells)

out3 <- here("output",
            "RDS", "AllBatches_Final_Clusters_SEUs",
             paste0("G000231_Neeland_",tissue,".final_clusters.SEU.rds"))

if (!file.exists(out3)) {
  saveRDS(merged, file = out3)
}

Session Info

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.2 (2023-10-31)
 os       macOS Sonoma 14.6.1
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Australia/Melbourne
 date     2024-09-26
 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)
 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)
 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)
 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)
 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)
 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 14.6.1

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              ica_1.0-3                  
 [33] spatstat.random_3.2-2       Matrix_1.6-5               
 [35] fansi_1.0.6                 abind_1.4-5                
 [37] lifecycle_1.0.4             whisker_0.4.1              
 [39] yaml_2.3.8                  SummarizedExperiment_1.32.0
 [41] SparseArray_1.2.4           Rtsne_0.17                 
 [43] paletteer_1.6.0             grid_4.3.2                 
 [45] blob_1.2.4                  promises_1.2.1             
 [47] crayon_1.5.2                miniUI_0.1.1.1             
 [49] lattice_0.22-5              cowplot_1.1.3              
 [51] KEGGREST_1.42.0             pillar_1.9.0               
 [53] knitr_1.45                  GenomicRanges_1.54.1       
 [55] future.apply_1.11.1         codetools_0.2-19           
 [57] leiden_0.4.3.1              getPass_0.2-4              
 [59] vctrs_0.6.5                 png_0.1-8                  
 [61] spam_2.10-0                 cellranger_1.1.0           
 [63] gtable_0.3.4                rematch2_2.1.2             
 [65] cachem_1.0.8                xfun_0.42                  
 [67] S4Arrays_1.2.0              mime_0.12                  
 [69] tidygraph_1.3.1             survival_3.5-8             
 [71] SingleCellExperiment_1.24.0 statmod_1.5.0              
 [73] ellipsis_0.3.2              fitdistrplus_1.1-11        
 [75] ROCR_1.0-11                 nlme_3.1-164               
 [77] bit64_4.0.5                 RcppAnnoy_0.0.22           
 [79] GenomeInfoDb_1.38.6         rprojroot_2.0.4            
 [81] bslib_0.6.1                 irlba_2.3.5.1              
 [83] KernSmooth_2.23-22          colorspace_2.1-0           
 [85] DBI_1.2.2                   tidyselect_1.2.0           
 [87] processx_3.8.3              bit_4.0.5                  
 [89] compiler_4.3.2              git2r_0.33.0               
 [91] xml2_1.3.6                  DelayedArray_0.28.0        
 [93] plotly_4.10.4               checkmate_2.3.1            
 [95] scales_1.3.0                lmtest_0.9-40              
 [97] callr_3.7.5                 digest_0.6.34              
 [99] goftest_1.2-3               spatstat.utils_3.0-4       
[101] presto_1.0.0                rmarkdown_2.25             
[103] XVector_0.42.0              htmltools_0.5.7            
[105] pkgconfig_2.0.3             MatrixGenerics_1.14.0      
[107] highr_0.10                  fastmap_1.1.1              
[109] rlang_1.1.3                 htmlwidgets_1.6.4          
[111] shiny_1.8.0                 farver_2.1.1               
[113] jquerylib_0.1.4             zoo_1.8-12                 
[115] jsonlite_1.8.8              RCurl_1.98-1.14            
[117] magrittr_2.0.3              GenomeInfoDbData_1.2.11    
[119] dotCall64_1.1-1             munsell_0.5.0              
[121] Rcpp_1.0.12                 viridis_0.6.5              
[123] reticulate_1.35.0           stringi_1.8.3              
[125] zlibbioc_1.48.0             MASS_7.3-60.0.1            
[127] plyr_1.8.9                  parallel_4.3.2             
[129] listenv_0.9.1               ggrepel_0.9.5              
[131] deldir_2.0-2                Biostrings_2.70.2          
[133] graphlayouts_1.1.0          splines_4.3.2              
[135] tensor_1.5                  hms_1.1.3                  
[137] locfit_1.5-9.8              ps_1.7.6                   
[139] igraph_2.0.2                spatstat.geom_3.2-8        
[141] RcppHNSW_0.6.0              reshape2_1.4.4             
[143] evaluate_0.23               BiocManager_1.30.22        
[145] tzdb_0.4.0                  tweenr_2.0.3               
[147] httpuv_1.6.14               RANN_2.6.1                 
[149] polyclip_1.10-6             future_1.33.1              
[151] scattermore_1.2             ggforce_0.4.2              
[153] xtable_1.8-4                RSpectra_0.16-1            
[155] later_1.3.2                 viridisLite_0.4.2          
[157] memoise_2.0.1               cluster_2.1.6              
[159] timechange_0.3.0            globals_0.16.2