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.

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File Version Author Date Message
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

For Bronchial brushings, we used only Batch4 for the downstream analysis.

tissue <- "Bronchial_brushings"
out1 <- here("output",
            "RDS", "AllBatches_Clustering_SEUs",
             paste0("G000231_Neeland_",tissue,".Clusters.SEU.rds"))
seu_obj <- readRDS(out1)
seu_obj
An object of class Seurat 
18046 features across 33917 samples within 1 assay 
Active assay: RNA (18046 features, 2000 variable features)
 3 layers present: counts, data, scale.data
 3 dimensional reductions calculated: pca, umap, umap.unintegrated

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-

BB_Tcell_population_res.0.4

idx <- which(Idents(seu_obj) %in% c("CD4 T cells", "CD8 T cells", "proliferating T/NK"))
paed_sub <- seu_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 
18035 features across 7632 samples within 1 assay 
Active assay: RNA (18035 features, 1995 variable features)
 3 layers present: counts, data, scale.data
 3 dimensional reductions calculated: pca, umap, umap.unintegrated
paed_sub <- paed_sub %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() 
  
paed_sub <- RunUMAP(paed_sub, dims = 1:30, reduction = "pca", reduction.name = "umap.new")

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


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: 7632
Number of edges: 279916

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9447
Number of communities: 7
Elapsed time: 4 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7632
Number of edges: 279916

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9200
Number of communities: 9
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7632
Number of edges: 279916

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9041
Number of communities: 10
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7632
Number of edges: 279916

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8890
Number of communities: 11
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7632
Number of edges: 279916

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8743
Number of communities: 12
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7632
Number of edges: 279916

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8605
Number of communities: 13
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7632
Number of edges: 279916

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8484
Number of communities: 15
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7632
Number of edges: 279916

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8392
Number of communities: 18
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7632
Number of edges: 279916

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8308
Number of communities: 20
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7632
Number of edges: 279916

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8230
Number of communities: 21
Elapsed time: 2 seconds
DimHeatmap(paed_sub, dims = 1:10, cells = 500, balanced = TRUE)

Version Author Date
07af966 Gunjan Dixit 2024-09-25 
clustree(paed_sub, prefix = "RNA_snn_res.")

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
paed_sub$cluster <- paed_sub$RNA_snn_res.0.4
Idents(paed_sub) <- paed_sub$cluster
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
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"    "HOPX"    "MAF"     "TCF7"    "LAG3"    "TYROBP"  "UHRF1"  
 [8] "CSF3R"   "NOG"     "POU2AF1" "GPNMB"

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

FeaturePlot(paed_sub,features=best.wilcox.gene.per.cluster, reduction = 'umap.new', raster = FALSE, ncol = 2, label = TRUE)

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.

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 = "BB")
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"))

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))

labels <- c( "predicted.ann_level_1","predicted.ann_level_2", "predicted.ann_level_3", "predicted.ann_level_4", "predicted.ann_level_5","predicted.ann_finest_level", "cell_labels", "cell_labels_v2", "RNA_snn_res.0.4")

p <- vector("list",length(labels))
for(label in labels){
  paed_sub@meta.data %>%
    ggplot(aes(x = !!sym(label), 
               fill = !!sym(label))) +
    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()) +
    NoLegend() +
    labs(y = "No. Cells (log scale)") -> p1
  
  paed_sub@meta.data %>%
    dplyr::select(!!sym(label), Sample) %>%
    group_by(!!sym(label), Sample) %>%
    summarise(num = n()) %>%
    mutate(prop = num / sum(num)) %>%
  ggplot(aes(x = !!sym(label), 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) -> p2
  
  (p1 / p2) & theme(legend.text = element_text(size = 8),
                  legend.key.size = unit(3, "mm")) -> p[[label]]
}
`summarise()` has grouped output by 'predicted.ann_level_1'. You can override
using the `.groups` argument.
`summarise()` has grouped output by 'predicted.ann_level_2'. You can override
using the `.groups` argument.
`summarise()` has grouped output by 'predicted.ann_level_3'. You can override
using the `.groups` argument.
`summarise()` has grouped output by 'predicted.ann_level_4'. You can override
using the `.groups` argument.
`summarise()` has grouped output by 'predicted.ann_level_5'. You can override
using the `.groups` argument.
`summarise()` has grouped output by 'predicted.ann_finest_level'. You can
override using the `.groups` argument.
`summarise()` has grouped output by 'cell_labels'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'cell_labels_v2'. You can override using
the `.groups` argument.
`summarise()` has grouped output by 'RNA_snn_res.0.4'. You can override using
the `.groups` argument.
p
[[1]]
NULL

[[2]]
NULL

[[3]]
NULL

[[4]]
NULL

[[5]]
NULL

[[6]]
NULL

[[7]]
NULL

[[8]]
NULL

[[9]]
NULL

$predicted.ann_level_1
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.

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

$predicted.ann_level_2

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

$predicted.ann_level_3

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

$predicted.ann_level_4

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

$predicted.ann_level_5

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

$predicted.ann_finest_level

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

$cell_labels

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

$cell_labels_v2

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

$RNA_snn_res.0.4

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

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

Additional QC plots for Proliferatink T/NK cluster

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

Version Author Date
07af966 Gunjan Dixit 2024-09-25 
DimPlot(seu_obj, reduction = "umap", group.by = "predicted.ann_level_4", label = TRUE)

Version Author Date
07af966 Gunjan Dixit 2024-09-25 
cl_13 <- as.data.frame(table(seu_obj$predicted.ann_level_4[seu_obj$RNA_snn_res.0.4 == "13"]))

colnames(cl_13) <- c("CellType", "Count")
cl_13 <- cl_13 %>% filter(Count > 0)
ggplot(cl_13, 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 Cluster 13", x = "Cell Type", y = "Count") +
theme_minimal() +
theme(legend.position = "none")

Version Author Date
07af966 Gunjan Dixit 2024-09-25 
FeaturePlot(seu_obj, features = c('nCount_RNA',"nFeature_RNA"), pt.size=0.2, label = T, ncol =2)

Version Author Date
07af966 Gunjan Dixit 2024-09-25 
VlnPlot(seu_obj,features = c("nCount_RNA","nFeature_RNA"), pt.size = 0) &
theme(plot.title = element_text(size=10))

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)
}

Other Clusters (excluding subclusters)

idx <- which(Idents(seu_obj) %in% c("CD4 T cells", "CD8 T cells", "proliferating T/NK"))
paed_sub <- seu_obj[,-idx]
paed_sub
An object of class Seurat 
18046 features across 26285 samples within 1 assay 
Active assay: RNA (18046 features, 2000 variable features)
 3 layers present: counts, data, scale.data
 3 dimensional reductions calculated: pca, umap, umap.unintegrated
levels(paed_sub$cell_labels)[levels(paed_sub$cell_labels) == "goblet/club/basal cells"] <- "non-ciliated cells"
levels(Idents(paed_sub))[levels(Idents(paed_sub)) == "goblet/club/basal cells"] <- "non-ciliated cells"
paed_sub$cell_labels_v2 <- Idents(paed_sub)
# Visualize the clustering results
DimPlot(paed_sub, reduction = "umap", group.by = "cluster", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

Version Author Date
07af966 Gunjan Dixit 2024-09-25 
DimPlot(paed_sub, reduction = "umap", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

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_sub, file = out2)
}

Merge seurat objects of subclusters

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

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

seu <- merge(seuLst[[1]], 
             y = seuLst[[2]])
seu
An object of class Seurat 
18046 features across 33917 samples within 1 assay 
Active assay: RNA (18046 features, 2000 variable features)
 6 layers present: counts.1, counts.2, data.1, scale.data.1, data.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:  TNFRSF13B, CD44, ADGRE5, DUSP4, ITGAX, MPEG1, EMP3, KLF2, NR4A1, ZEB2 
       LITAF, SEMA7A, PREX1, NR4A2, BHLHE40, VIM, TSC22D3, ZBTB32, JUNB, IER5 
       TYMP, FCRL4, RIN3, KDM6B, SOCS3, CCR6, CCDC50, FOSL2, NLRC5, FCRL5 
Negative:  MKI67, MEF2B, KIFC1, HIST1H1B, HMCES, AURKB, DTX1, BIRC5, TOP2A, MYBL1 
       UHRF1, NUGGC, TK1, STMN1, CDK1, MYBL2, TPX2, HMGB2, BUB1, HJURP 
       E2F2, MARCKSL1, TYMS, CDT1, DCAF12, AFF2, TCL1A, NUSAP1, HIST1H2BH, CCNB2 
PC_ 2 
Positive:  IGHM, IGHD, NIBAN3, CD72, P2RX5, TCL1A, MMP17, PNPLA7, FCMR, OBSCN 
       FCER2, MYO15B, CXCR4, FOXP1, CR2, TREML2, KLHL14, TENT5C, VPREB3, MARCKSL1 
       NEIL1, CD200, CRIP3, KCNG1, DIPK1B, C16orf74, LY9, CNR2, TP53INP1, HAPLN3 
Negative:  FCRL4, ITGAX, CCR1, KCTD12, CCR5, GSN, ITGB7, TESC, BHLHE40, IL2RB 
       ADGRG5, RAB31, HCK, C12orf75, BHLHE41, VIM, PLD4, SOX5, IVNS1ABP, TPM4 
       MYO1F, PEA15, FCRL5, EFHD2, ZYX, PREX1, CMTM6, SEMA7A, SIGLEC6, RUNX2 
PC_ 3 
Positive:  JCHAIN, CHPF, XBP1, MZB1, DERL3, AQP3, CKAP4, FNDC3B, ERN1, PRDM1 
       ITM2C, HID1, TXNDC5, SDF2L1, SSR4, HSP90B1, FKBP11, SEC11C, SELENOS, MANF 
       RRBP1, WFS1, CRELD2, HM13, MAN1A1, HPGD, NT5DC2, IGHA1, HYOU1, SELPLG 
Negative:  LTB, CD83, CXCR4, SPIB, NR4A1, CIITA, KDM6B, NFKBID, NR4A3, EGR3 
       KIFC1, NR4A2, METRNL, AURKB, ADGRE5, IER5, ITGAX, DUSP4, FOSL2, MKI67 
       TPX2, ARHGAP9, INF2, CDC20, BIRC5, KLF6, TOP2A, DUSP2, HIST1H1B, CDK1 
PC_ 4 
Positive:  TLR10, TRIM22, PLAC8, SIGLEC14, XAF1, STAT1, IFI44L, FCRL1, CCR6, SAMD9L 
       CIITA, RESF1, GAPT, SELL, HHEX, CMPK2, CHI3L2, EIF2AK2, OAS2, CD24 
       IFIT3, IFI44, DOCK10, OAS1, MX1, TNFSF10, FCRL2, MX2, SLFN5, CD53 
Negative:  KDM6B, FOSL2, NR4A3, NFKBID, NR4A1, DUSP4, SRGN, SLC7A5, PIM3, NR4A2 
       EGR3, DUSP2, IRF4, LMNA, PER1, GRASP, FOSB, TRAF1, ADGRE5, SLCO4A1 
       NINJ1, CD83, CD70, JUNB, TXNDC5, MYC, CREM, RGCC, BCL2L11, BICDL1 
PC_ 5 
Positive:  ISG15, IFI6, OAS3, IFIT1, IRF7, APOL6, CMPK2, LY6E, USP18, IFI44L 
       XAF1, MX1, WARS, IFIT3, MX2, IFIT2, TRIM22, OAS2, IFI44, RSAD2 
       GBP1, STAT1, HAPLN3, UBE2L6, STAT2, GBP4, HELZ2, HERC5, LAP3, IFI35 
Negative:  FCRL2, FCRL4, KCTD12, IGHA1, MGAT4A, METTL7A, CD180, MYL9, RFLNB, ADGRG5 
       CD27, CAMK1, IGHA2, MS4A7, DERL3, LGALS1, SMIM14, TNFRSF17, CLIP2, ITGB7 
       IFNGR1, RHOQ, RUNX2, MAML3, TLR10, CMTM3, LTB, APCDD1, HHEX, SNX22 
paed_bcells <- RunUMAP(paed_bcells, dims = 1:30, reduction = "pca", reduction.name = "umap.bcell")
10:50:45 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'
10:50:45 Read 2671 rows and found 30 numeric columns
10:50:45 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'
10:50:45 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:50:45 Writing NN index file to temp file /var/folders/q8/kw1r78g12qn793xm7g0zvk94x2bh70/T//RtmpS8etyp/file624d707c805c
10:50:45 Searching Annoy index using 1 thread, search_k = 3000
10:50:46 Annoy recall = 100%
10:50:46 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
10:50:47 Initializing from normalized Laplacian + noise (using RSpectra)
10:50:47 Commencing optimization for 500 epochs, with 111186 positive edges
10:50:49 Optimization finished
DimPlot(paed_bcells, group.by = "cell_labels_v2",raster = FALSE, repel = TRUE, label = TRUE, label.size = 4.5)

Version Author Date
07af966 Gunjan Dixit 2024-09-25 
DimPlot(paed_bcells,raster = FALSE, repel = TRUE, label = TRUE, label.size = 4.5)

Reclustering B cells

The marker genes for this reclustering can be found here-

BB_Bcell_population_res.0.3

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: 2671
Number of edges: 100594

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.9315
Number of communities: 4
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2671
Number of edges: 100594

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8899
Number of communities: 5
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2671
Number of edges: 100594

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8659
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2671
Number of edges: 100594

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8494
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2671
Number of edges: 100594

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8349
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2671
Number of edges: 100594

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8210
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2671
Number of edges: 100594

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.8086
Number of communities: 10
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2671
Number of edges: 100594

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.7982
Number of communities: 11
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2671
Number of edges: 100594

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.7878
Number of communities: 12
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2671
Number of edges: 100594

Running smart local moving algorithm...
Maximum modularity in 10 random starts: 0.7775
Number of communities: 12
Elapsed time: 0 seconds
DimHeatmap(paed_bcells, dims = 1:10, cells = 500, balanced = TRUE)

clustree(paed_bcells, prefix = "RNA_snn_res.")

opt_res <- "RNA_snn_res.0.3"  
n <- nlevels(paed_bcells$RNA_snn_res.0.3)
paed_bcells$RNA_snn_res.0.3 <- factor(paed_bcells$RNA_snn_res.0.3, levels = seq(0,n-1))
paed_bcells$seurat_clusters <- NULL
Idents(paed_bcells) <- paed_bcells$RNA_snn_res.0.3
DimPlot(paed_bcells, group.by = "RNA_snn_res.0.3", label = TRUE, label.size = 4.5, repel = TRUE, raster = FALSE )

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
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] "IGHD"  "KDM6B" "MS4A1" "FCRL4" "MEF2B" "UHRF1" "CHPF" 
FeaturePlot(paed_bcells,features=best.wilcox.gene.per.cluster, raster = FALSE, label = T, ncol = 3)

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.

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:  C9orf24, SNTN, C20orf85, PIFO, RSPH1, ZMYND10, FOXJ1, LRRC46, SPATA18, TEKT1 
       DNALI1, FAM92B, ROPN1L, LRRC10B, C5orf49, RSPH4A, AC007906.2, CAPSL, C11orf88, FAM216B 
       C9orf116, DRC3, B9D1, LDLRAD1, EFCAB1, ERICH3, WDR66, HYDIN, SAXO2, VWA3A 
Negative:  VIM, ZEB2, SPI1, PFN1, THEMIS2, COTL1, FCER1G, TYROBP, IFI30, FGR 
       IRF8, CD68, CD83, LGALS1, ALOX5, GRN, ITGAX, EMILIN2, METRNL, HCK 
       CYBB, MS4A7, AIF1, CXCR4, FCGR3A, TFRC, LYZ, PHACTR1, C1QB, HAVCR2 
PC_ 2 
Positive:  KRT7, F3, CREB3L1, FAM3D, S100A16, PRSS8, MDK, CEACAM6, C3, SDC1 
       RDH10, MUC1, SLC6A14, BPIFB1, S100P, ALPL, WFDC2, SLC5A8, A4GALT, MUC5B 
       ASS1, VSIG2, LCN2, SERPINB3, TMPRSS4, UPK1B, GPC1, GPRC5A, FCGBP, CXCL6 
Negative:  VIM, KCTD12, SPI1, FCER1G, TYROBP, CD68, CTSZ, LGALS1, CD4, ZEB2 
       FGR, EMILIN2, MS4A7, THEMIS2, CD83, AIF1, IRF8, COTL1, CYBB, HCK 
       C1QB, S100A10, ALOX5, FCGR3A, IFI30, SLC15A3, TFRC, DSE, ITGAX, HMOX1 
PC_ 3 
Positive:  CST3, LYZ, ALDH2, GRN, CEBPB, CD68, IFI30, SLC15A3, TYROBP, FCER1G 
       EMILIN2, MS4A7, LMNA, C1QB, CTSZ, TGM2, AIF1, HMOX1, S100A9, TYMP 
       RBM47, SPI1, FCGR3A, MS4A4A, RHOB, LGALS1, DSE, TFRC, HCK, AXL 
Negative:  IL32, CCL5, NKG7, PRF1, TRBC1, GZMA, LTB, CD69, HOPX, GNLY 
       TIGIT, CCND2, CD27, CST7, FASLG, CXCR4, CD5, ARL4C, ITM2C, IGHM 
       GZMB, PIM2, LAG3, P2RX5, CD79A, TCF7, HIST1H1C, LY9, KLRC1, TBX21 
PC_ 4 
Positive:  CCL5, IL32, PRF1, GZMA, TRBC1, NKG7, GNLY, FASLG, CST7, TIGIT 
       ANXA1, KLRC1, TNFAIP3, CSF1, LAG3, CCL4, CD5, IL7R, HOPX, GZMH 
       MAF, TNFSF14, CRTAM, KLRB1, TCF7, NCR1, JUN, PHLDA1, TRDC, IFNG 
Negative:  CD79A, MS4A1, TNFRSF13C, CD19, PAX5, BANK1, SPIB, TNFRSF13B, CD79B, CD22 
       FCRL5, IGKC, WDFY4, POU2AF1, FCRLA, CXCR5, BLNK, TLR10, IGHG1, CLEC17A 
       PLD4, BLK, BCL11A, FCRL2, IGHM, POU2F2, TSPAN33, CIITA, MPEG1, IGHA1 
PC_ 5 
Positive:  BHLHE41, C1QB, PPARG, RBP4, ALDH2, C8B, FABP5, FN1, TFRC, CRIP1 
       AGRP, LDHB, HSPB1, BCAR1, CA2, LMNA, CCL18, INF2, YBX1, STMN1 
       LY86, CHDH, TUBB, MS4A7, NME2, CAMP, PTMS, GAS2L3, MS4A4A, AXL 
Negative:  APOBEC3A, IDO1, ISG15, ISG20, RSAD2, MX2, SERPINB9, CXCL11, IRF7, LILRA5 
       CD300E, CXCL10, IFIT2, SOCS3, IFITM3, CSF3R, SPHK1, GBP1, SAT1, GBP5 
       IFIT1, MX1, IFI44L, CMPK2, NLRP3, CCL8, HELZ2, CALHM6, PFKFB3, OAS3 
merged <- RunUMAP(merged, dims = 1:30, reduction = "pca", reduction.name = "umap.merged")
10:51:25 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'
10:51:25 Read 33552 rows and found 30 numeric columns
10:51:25 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'
10:51:25 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:51:27 Writing NN index file to temp file /var/folders/q8/kw1r78g12qn793xm7g0zvk94x2bh70/T//RtmpS8etyp/file624d52e5b43
10:51:27 Searching Annoy index using 1 thread, search_k = 3000
10:51:32 Annoy recall = 100%
10:51:33 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
10:51:33 Initializing from normalized Laplacian + noise (using RSpectra)
10:51:39 Commencing optimization for 200 epochs, with 1419026 positive edges
10:51:49 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

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)
 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)
 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

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

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] ggbeeswarm_0.7.2            fansi_1.0.6                
 [37] abind_1.4-5                 lifecycle_1.0.4            
 [39] whisker_0.4.1               yaml_2.3.8                 
 [41] SummarizedExperiment_1.32.0 SparseArray_1.2.4          
 [43] Rtsne_0.17                  paletteer_1.6.0            
 [45] grid_4.3.2                  blob_1.2.4                 
 [47] promises_1.2.1              crayon_1.5.2               
 [49] miniUI_0.1.1.1              lattice_0.22-5             
 [51] cowplot_1.1.3               KEGGREST_1.42.0            
 [53] pillar_1.9.0                knitr_1.45                 
 [55] GenomicRanges_1.54.1        future.apply_1.11.1        
 [57] codetools_0.2-19            leiden_0.4.3.1             
 [59] getPass_0.2-4               vctrs_0.6.5                
 [61] png_0.1-8                   spam_2.10-0                
 [63] cellranger_1.1.0            gtable_0.3.4               
 [65] rematch2_2.1.2              cachem_1.0.8               
 [67] xfun_0.42                   S4Arrays_1.2.0             
 [69] mime_0.12                   tidygraph_1.3.1            
 [71] survival_3.5-8              SingleCellExperiment_1.24.0
 [73] statmod_1.5.0               ellipsis_0.3.2             
 [75] fitdistrplus_1.1-11         ROCR_1.0-11                
 [77] nlme_3.1-164                bit64_4.0.5                
 [79] RcppAnnoy_0.0.22            GenomeInfoDb_1.38.6        
 [81] rprojroot_2.0.4             bslib_0.6.1                
 [83] irlba_2.3.5.1               vipor_0.4.7                
 [85] KernSmooth_2.23-22          colorspace_2.1-0           
 [87] DBI_1.2.2                   ggrastr_1.0.2              
 [89] tidyselect_1.2.0            processx_3.8.3             
 [91] bit_4.0.5                   compiler_4.3.2             
 [93] git2r_0.33.0                xml2_1.3.6                 
 [95] DelayedArray_0.28.0         plotly_4.10.4              
 [97] checkmate_2.3.1             scales_1.3.0               
 [99] lmtest_0.9-40               callr_3.7.5                
[101] digest_0.6.34               goftest_1.2-3              
[103] spatstat.utils_3.0-4        presto_1.0.0               
[105] rmarkdown_2.25              XVector_0.42.0             
[107] htmltools_0.5.7             pkgconfig_2.0.3            
[109] MatrixGenerics_1.14.0       highr_0.10                 
[111] fastmap_1.1.1               rlang_1.1.3                
[113] htmlwidgets_1.6.4           shiny_1.8.0                
[115] farver_2.1.1                jquerylib_0.1.4            
[117] zoo_1.8-12                  jsonlite_1.8.8             
[119] RCurl_1.98-1.14             magrittr_2.0.3             
[121] GenomeInfoDbData_1.2.11     dotCall64_1.1-1            
[123] munsell_0.5.0               Rcpp_1.0.12                
[125] viridis_0.6.5               reticulate_1.35.0          
[127] stringi_1.8.3               zlibbioc_1.48.0            
[129] MASS_7.3-60.0.1             plyr_1.8.9                 
[131] parallel_4.3.2              listenv_0.9.1              
[133] ggrepel_0.9.5               deldir_2.0-2               
[135] Biostrings_2.70.2           graphlayouts_1.1.0         
[137] splines_4.3.2               tensor_1.5                 
[139] hms_1.1.3                   locfit_1.5-9.8             
[141] ps_1.7.6                    igraph_2.0.2               
[143] spatstat.geom_3.2-8         RcppHNSW_0.6.0             
[145] reshape2_1.4.4              evaluate_0.23              
[147] BiocManager_1.30.22         tzdb_0.4.0                 
[149] tweenr_2.0.3                httpuv_1.6.14              
[151] RANN_2.6.1                  polyclip_1.10-6            
[153] future_1.33.1               scattermore_1.2            
[155] ggforce_0.4.2               xtable_1.8-4               
[157] RSpectra_0.16-1             later_1.3.2                
[159] viridisLite_0.4.2           beeswarm_0.4.0             
[161] memoise_2.0.1               cluster_2.1.6              
[163] timechange_0.3.0            globals_0.16.2