DE genes between T cell groups

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Last updated: 2022-12-12

Checks: 6 1

Knit directory: humanCardiacFibroblasts/

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File	Version	Author	Date	Message
Rmd	307a2ca	mluetge	2022-12-12	update pat ID SG29
html	307a2ca	mluetge	2022-12-12	update pat ID SG29
Rmd	66c2208	mluetge	2022-12-08	compare T cell groups and project gene signatures
html	66c2208	mluetge	2022-12-08	compare T cell groups and project gene signatures

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load packages

suppressPackageStartupMessages({
  library(SingleCellExperiment)
  library(tidyverse)
  library(Seurat)
  library(magrittr)
  library(dplyr)
  library(purrr)
  library(ggplot2)
  library(here)
  library(runSeurat3)
  library(ggsci)
  library(pheatmap)
  library(ggpubr)
  library(RColorBrewer)
  library(viridis)
})

heatmap function

avgHeatmap <- function(seurat, selGenes, colVecIdent, colVecCond=NULL,
                       ordVec=NULL, gapVecR=NULL, gapVecC=NULL,cc=FALSE,
                       cr=FALSE, condCol=FALSE){
  
  selGenes <- selGenes$gene
  
  ## assay data
  clusterAssigned <- as.data.frame(Idents(seurat)) %>%
  dplyr::mutate(cell=rownames(.))
  colnames(clusterAssigned)[1] <- "ident"
  seuratDat <- GetAssayData(seurat)
  
  ## genes of interest
  genes <- data.frame(gene=rownames(seurat)) %>% 
    mutate(geneID=gsub("^.*\\.", "", gene)) %>% filter(geneID %in% selGenes)

  ## matrix with averaged cnts per ident
  logNormExpres <- as.data.frame(t(as.matrix(
    seuratDat[which(rownames(seuratDat) %in% genes$gene),])))
  logNormExpres <- logNormExpres %>% dplyr::mutate(cell=rownames(.)) %>%
    dplyr::left_join(.,clusterAssigned, by=c("cell")) %>%
    dplyr::select(-cell) %>% dplyr::group_by(ident) %>%
    dplyr::summarise_all(mean)
  logNormExpresMa <- logNormExpres %>% dplyr::select(-ident) %>% as.matrix()
  rownames(logNormExpresMa) <- logNormExpres$ident
  logNormExpresMa <- t(logNormExpresMa)
  rownames(logNormExpresMa) <- gsub("^.*?\\.","",rownames(logNormExpresMa))
  
  ## remove genes if they are all the same in all groups
  ind <- apply(logNormExpresMa, 1, sd) == 0
  logNormExpresMa <- logNormExpresMa[!ind,]
  genes <- genes[!ind,]

  ## color columns according to cluster
  annotation_col <- as.data.frame(gsub("(^.*?_)","",
                                       colnames(logNormExpresMa)))%>%
    dplyr::mutate(celltype=gsub("(_.*$)","",colnames(logNormExpresMa)))
  colnames(annotation_col)[1] <- "col1"
  annotation_col <- annotation_col %>%
    dplyr::mutate(cond = gsub(".*_","",col1)) %>%
    dplyr::select(cond, celltype)
  rownames(annotation_col) <- colnames(logNormExpresMa) 

  ann_colors = list(
      cond = colVecCond,
      celltype=colVecIdent)
  if(is.null(ann_colors$cond)){
    annotation_col$cond <- NULL
  }
  
  ## adjust order
  logNormExpresMa <- logNormExpresMa[selGenes,]
  if(is.null(ordVec)){
    ordVec <- levels(seurat)
  }
  logNormExpresMa <- logNormExpresMa[,ordVec]

  ## scaled row-wise
  pheatmap(logNormExpresMa, scale="row" ,treeheight_row = 0, cluster_rows = cr, 
         cluster_cols = cc, border_color = NA, 
         color = colorRampPalette(c("#2166AC", "#F7F7F7", "#B2182B"))(50),
         annotation_col = annotation_col, cellwidth=15, cellheight=10,
         annotation_colors = ann_colors, gaps_row = gapVecR, gaps_col = gapVecC)
}

sign plot funct

## adapted from CellMixS
visGroup_adapt <- function (sce,group,dim_red = "TSNE",col_group=pal_nejm()(8)) 
{
    if (!is(sce, "SingleCellExperiment")) {
        stop("Error:'sce' must be a 'SingleCellExperiment' object.")
    }
    if (!group %in% names(colData(sce))) {
        stop("Error: 'group' variable must be in 'colData(sce)'")
    }
    cell_names <- colnames(sce)
    if (!dim_red %in% "TSNE") {
        if (!dim_red %in% reducedDimNames(sce)) {
            stop("Please provide a dim_red method listed in reducedDims of sce")
        }
        red_dim <- as.data.frame(reducedDim(sce, dim_red))
    }
    else {
        if (!"TSNE" %in% reducedDimNames(sce)) {
            if ("logcounts" %in% names(assays(sce))) {
                sce <- runTSNE(sce)
            }
            else {
                sce <- runTSNE(sce, exprs_values = "counts")
            }
        }
        red_dim <- as.data.frame(reducedDim(sce, "TSNE"))
    }
    colnames(red_dim) <- c("red_dim1", "red_dim2")
    df <- data.frame(sample_id = cell_names, group_var = colData(sce)[, 
        group], red_Dim1 = red_dim$red_dim1, red_Dim2 = red_dim$red_dim2)
    t <- ggplot(df, aes_string(x = "red_Dim1", y = "red_Dim2")) + 
        xlab(paste0(dim_red, "_1")) + ylab(paste0(dim_red, "_2")) + 
        theme_void() + theme(aspect.ratio = 1,
                             panel.grid.minor = element_blank(), 
        panel.grid.major = element_line(color = "grey", size = 0.3))
    t_group <- t + geom_point(size = 1.5, alpha = 0.8,
                              aes_string(color = "group_var")) + 
        guides(color = guide_legend(override.aes = list(size = 1), 
            title = group)) + ggtitle(group)
    if (is.numeric(df$group_var)) {
        t_group <- t_group + scale_color_viridis(option = "D")
    }
    else {
        t_group <- t_group + scale_color_manual(values = col_group)
    }
    t_group
}

set dir

basedir <- here()
seurat <- readRDS(file = paste0(basedir, 
                              "/data/humanHeartsPlusGraz_intPatients_merged", 
                              "labeled_woHH_seurat.rds"))
Idents(seurat) <- seurat$integrated_snn_res.0.6

color vectors

colPal <- c(pal_igv()(12),
            pal_aaas()(10))[1:length(levels(seurat))]
colTec <- pal_jama()(length(unique(seurat$technique)))
colSmp <- c(pal_uchicago()(9), pal_npg()(10), pal_aaas()(10), 
            pal_jama()(7))[1:length(unique(seurat$dataset))]
colCond <- pal_npg()(length(unique(seurat$cond)))
colID <- c(pal_jco()(10), pal_npg()(10), pal_futurama()(10),
           pal_d3()(10))[1:length(unique(seurat$ID))]
colOrig <- pal_aaas()(length(unique(seurat$origin)))
colIso <- pal_nejm()(length(unique(seurat$isolation)))
colProc <- pal_aaas()(length(unique(seurat$processing)))
colTgrp <- c("#d70700", "#00239a", "#1f7a1f")
colLab <- c("#c08b65", "#ba4e45", "#d4cc84", "#546f82", "#5c5cdf",
            "#80396e", "#8d5639", "#779462", "#800000FF", "#d87c15",
            "#FFA319FF", "#FF95A8FF") 
names(colLab) <- c("EndoEC", "Tcell","resMacrophage", "Fibroblast",
                   "infMacrophage", "Perivascular","Cardiomyocyte",
                   "Endothelial","Adipocytes","NeuralCells","SMC","LEC")
names(colTgrp) <- c("TcellHigh", "TcellLow", "TcellInt" )
names(colPal) <- levels(seurat)
names(colTec) <- unique(seurat$technique)
names(colSmp) <- unique(seurat$dataset)
names(colCond) <- unique(seurat$cond)
names(colID) <- unique(seurat$ID)
names(colOrig) <- unique(seurat$origin)
names(colIso) <- unique(seurat$isolation)
names(colProc) <- unique(seurat$processing)

vis data

clusters

DimPlot(seurat, reduction = "umap", cols=colPal)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis cluster-1.png"

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DimPlot(seurat, reduction = "umap", cols=colPal,
        shuffle = T)+
  theme_void()

Past versions of "vis cluster-2.png"

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label

DimPlot(seurat, reduction = "umap",  group.by = "label", cols=colLab)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

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DimPlot(seurat, reduction = "umap", group.by = "label", cols=colLab,
        shuffle = T)+
  theme_void()

Past versions of "vis label-2.png"

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ID

DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colID, shuffle = T)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis ID-1.png"

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DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colID,
        shuffle = T)+
  theme_void()

Past versions of "vis ID-2.png"

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cond

DimPlot(seurat, reduction = "umap", group.by = "cond", cols=colCond)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis cond-1.png"

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T cell grp

DimPlot(seurat, reduction = "umap", group.by = "TcellGrp", cols=colTgrp)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

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DimPlot(seurat, reduction = "umap", group.by = "TcellGrp", cols=colTgrp,
        shuffle = T)+
  theme_void()

Past versions of "vis T cell group-2.png"

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label split by T cell Grp

DimPlot(seurat, reduction = "umap", group.by = "label", cols=colLab,
        split.by = "TcellGrp", shuffle = T)+
  theme_void()

Past versions of "vis label split seq-1.png"

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project signatures

signature cut 2

signDat <- read_delim(file = paste0(basedir,
                    "/data/GSEA/selGenesSignature.txt"),
                    delim = "\t")
genes <- data.frame(geneID=rownames(seurat)) %>% 
  mutate(gene=gsub("^.*\\.", "", geneID))
signDat <- signDat %>% left_join(.,genes, by="gene")
allSign <- unique(signDat$signature)

sce <- as.SingleCellExperiment(seurat)

## add reduced dim
seurat2 <- seurat
DefaultAssay(object = seurat2) <- "integrated"
sce2 <- as.SingleCellExperiment(seurat2)
reducedDims(sce) <- reducedDims(sce2)
remove(seurat2)
remove(sce2)

treatGrps <- unique(sce$TcellGrp)

cutOff <- 2
pal = viridis(100)
sc <- scale_colour_gradientn(colours = pal, limits=c(0, cutOff))

lapply(unique(signDat$signature), function(sign){
  signGenes <- signDat %>% dplyr::filter(signature == sign)
  sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
  cntMat <- rowSums(t(as.matrix(sceSub@assays@data$logcounts)))/nrow(signGenes)
  sceSub$sign <- cntMat
  sceSub$sign[which(sceSub$sign > cutOff)] <- cutOff
  sceSub$sign[which(sceSub$sign < 0)] <- 0
  lapply(treatGrps, function(treat){
    sceSubT <- sceSub[, which(sceSub$TcellGrp == treat)]
    p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
    sc +
    guides(colour = guide_colourbar(title = '')) +
    ggtitle(paste0(sign, ' signature - ', treat)) +
    theme_classic() + 
    theme(axis.text = element_blank(),
          axis.ticks = element_blank()) +
    labs(x='Dimension 1', y='Dimension 2')
    p
  })
})

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pal = colorRampPalette(rev(brewer.pal(11, 'RdBu')))
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, cutOff))

lapply(unique(signDat$signature), function(sign){
  signGenes <- signDat %>% dplyr::filter(signature == sign)
  sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
  cntMat <- rowSums(t(as.matrix(sceSub@assays@data$logcounts)))/nrow(signGenes)
  sceSub$sign <- cntMat
  sceSub$sign[which(sceSub$sign > cutOff)] <- cutOff
  sceSub$sign[which(sceSub$sign < 0)] <- 0
  lapply(treatGrps, function(treat){
    sceSubT <- sceSub[, which(sceSub$TcellGrp == treat)]
    p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
    sc +
    guides(colour = guide_colourbar(title = '')) +
    ggtitle(paste0(sign, ' signature - ', treat)) +
    theme_classic() + 
    theme(axis.text = element_blank(),
          axis.ticks = element_blank()) +
    labs(x='Dimension 1', y='Dimension 2')
    p
  })
})

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signature cut 1.5

cutOff <- 1.5
pal = colorRampPalette(rev(brewer.pal(11, 'RdBu')))
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, cutOff))

lapply(unique(signDat$signature), function(sign){
  signGenes <- signDat %>% dplyr::filter(signature == sign)
  sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
  cntMat <- rowSums(t(as.matrix(sceSub@assays@data$logcounts)))/nrow(signGenes)
  sceSub$sign <- cntMat
  sceSub$sign[which(sceSub$sign > cutOff)] <- cutOff
  sceSub$sign[which(sceSub$sign < 0)] <- 0
  lapply(treatGrps, function(treat){
    sceSubT <- sceSub[, which(sceSub$TcellGrp == treat)]
    p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
    sc +
    guides(colour = guide_colourbar(title = '')) +
    ggtitle(paste0(sign, ' signature - ', treat)) +
    theme_classic() + 
    theme(axis.text = element_blank(),
          axis.ticks = element_blank()) +
    labs(x='Dimension 1', y='Dimension 2')
    p
  })
})

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pal = viridis(100)
sc <- scale_colour_gradientn(colours = pal, limits=c(0, cutOff))
lapply(unique(signDat$signature), function(sign){
  signGenes <- signDat %>% dplyr::filter(signature == sign)
  sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
  cntMat <- rowSums(t(as.matrix(sceSub@assays@data$logcounts)))/nrow(signGenes)
  sceSub$sign <- cntMat
  sceSub$sign[which(sceSub$sign > cutOff)] <- cutOff
  sceSub$sign[which(sceSub$sign < 0)] <- 0
  lapply(treatGrps, function(treat){
    sceSubT <- sceSub[, which(sceSub$TcellGrp == treat)]
    p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
    sc +
    guides(colour = guide_colourbar(title = '')) +
    ggtitle(paste0(sign, ' signature - ', treat)) +
    theme_classic() + 
    theme(axis.text = element_blank(),
          axis.ticks = element_blank()) +
    labs(x='Dimension 1', y='Dimension 2')
    p
  })
})

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signature cut 1

cutOff <- 1
pal = colorRampPalette(rev(brewer.pal(11, 'RdBu')))
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, cutOff))

lapply(unique(signDat$signature), function(sign){
  signGenes <- signDat %>% dplyr::filter(signature == sign)
  sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
  cntMat <- rowSums(t(as.matrix(sceSub@assays@data$logcounts)))/nrow(signGenes)
  sceSub$sign <- cntMat
  sceSub$sign[which(sceSub$sign > cutOff)] <- cutOff
  sceSub$sign[which(sceSub$sign < 0)] <- 0
  lapply(treatGrps, function(treat){
    sceSubT <- sceSub[, which(sceSub$TcellGrp == treat)]
    p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
    sc +
    guides(colour = guide_colourbar(title = '')) +
    ggtitle(paste0(sign, ' signature - ', treat)) +
    theme_classic() + 
    theme(axis.text = element_blank(),
          axis.ticks = element_blank()) +
    labs(x='Dimension 1', y='Dimension 2')
    p
  })
})

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pal = viridis(100)
sc <- scale_colour_gradientn(colours = pal, limits=c(0, cutOff))
lapply(unique(signDat$signature), function(sign){
  signGenes <- signDat %>% dplyr::filter(signature == sign)
  sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
  cntMat <- rowSums(t(as.matrix(sceSub@assays@data$logcounts)))/nrow(signGenes)
  sceSub$sign <- cntMat
  sceSub$sign[which(sceSub$sign > cutOff)] <- cutOff
  sceSub$sign[which(sceSub$sign < 0)] <- 0
  lapply(treatGrps, function(treat){
    sceSubT <- sceSub[, which(sceSub$TcellGrp == treat)]
    p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
    sc +
    guides(colour = guide_colourbar(title = '')) +
    ggtitle(paste0(sign, ' signature - ', treat)) +
    theme_classic() + 
    theme(axis.text = element_blank(),
          axis.ticks = element_blank()) +
    labs(x='Dimension 1', y='Dimension 2')
    p
  })
})

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project signatures on heatmap

signDat <- read_delim(file = paste0(basedir,
                    "/data/GSEA/selGenesSignature2.txt"),
                    delim = "\t")
genes <- data.frame(geneID=rownames(seurat)) %>% 
  mutate(gene=gsub("^.*\\.", "", geneID))
signDat <- signDat %>% left_join(.,genes, by="gene")

## only CM, Fibroblasts, Tcells and Myeloids
selLabel <- c("Tcell","resMacrophage", "Fibroblast","infMacrophage",
              "Cardiomyocyte")
seurat <- subset(seurat, label %in% selLabel)
seurat$label2 <- seurat$label
seurat$label2[which(seurat$label %in% c("resMacrophage",
                                        "infMacrophage"))] <- "Macrophage"
seurat$label2_plus_grp <- paste0(seurat$label2, "_", seurat$TcellGrp)
table(seurat$label2_plus_grp)

Cardiomyocyte_TcellHigh  Cardiomyocyte_TcellInt  Cardiomyocyte_TcellLow 
                   1023                    1338                     871 
   Fibroblast_TcellHigh     Fibroblast_TcellInt     Fibroblast_TcellLow 
                   2894                    5120                    3162 
   Macrophage_TcellHigh     Macrophage_TcellInt     Macrophage_TcellLow 
                   3530                    1162                     604 
        Tcell_TcellHigh          Tcell_TcellInt          Tcell_TcellLow 
                   4369                     768                     231 

seurat$label2_plus_grp <- as.factor(seurat$label2_plus_grp)
Idents(seurat) <- seurat$label2_plus_grp
gapVecCol <- seq(3, length(levels(seurat$label2_plus_grp)), by=3)
gapVecDat <- signDat %>% group_by(signature) %>% summarise(cnt=n())
gapVecRow <- cumsum(gapVecDat$cnt)

colLab2 <- c("#ba4e45", "#d4cc84", "#546f82", "#8d5639")
names(colLab2) <- c("Tcell","Macrophage","Fibroblast","Cardiomyocyte")
  
pOut <- avgHeatmap(seurat = seurat, selGenes = signDat,
                     colVecIdent = colLab2, colVecCond=colTgrp,
                     ordVec=levels(seurat),
                     gapVecR=gapVecRow, gapVecC=gapVecCol,cc=FALSE,
                     cr=F, condCol=T)

Past versions of "heatmap with signatures-1.png"

Version	Author	Date
307a2ca	mluetge	2022-12-12

seurat <- subset(seurat, TcellGrp %in% c("TcellHigh", "TcellLow"))

gapVecCol <- seq(2, length(unique(seurat$label2_plus_grp)), by=2)
gapVecDat <- signDat %>% group_by(signature) %>% summarise(cnt=n())
gapVecRow <- cumsum(gapVecDat$cnt)

pOut <- avgHeatmap(seurat = seurat, selGenes = signDat,
                     colVecIdent = colLab2, colVecCond=colTgrp,
                     ordVec=levels(seurat),
                     gapVecR=gapVecRow, gapVecC=gapVecCol,cc=FALSE,
                     cr=F, condCol=T)

session info

sessionInfo()

R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

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

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

other attached packages:
 [1] viridis_0.6.2               viridisLite_0.4.1          
 [3] RColorBrewer_1.1-3          ggpubr_0.5.0               
 [5] pheatmap_1.0.12             ggsci_2.9                  
 [7] runSeurat3_0.1.0            here_1.0.1                 
 [9] magrittr_2.0.3              SeuratObject_4.1.3         
[11] Seurat_4.3.0                forcats_0.5.2              
[13] stringr_1.5.0               dplyr_1.0.10               
[15] purrr_0.3.5                 readr_2.1.3                
[17] tidyr_1.2.1                 tibble_3.1.8               
[19] ggplot2_3.4.0               tidyverse_1.3.2            
[21] SingleCellExperiment_1.18.1 SummarizedExperiment_1.26.1
[23] Biobase_2.56.0              GenomicRanges_1.48.0       
[25] GenomeInfoDb_1.32.4         IRanges_2.30.1             
[27] S4Vectors_0.34.0            BiocGenerics_0.42.0        
[29] MatrixGenerics_1.8.1        matrixStats_0.63.0         

loaded via a namespace (and not attached):
  [1] utf8_1.2.2             spatstat.explore_3.0-5 reticulate_1.26       
  [4] tidyselect_1.2.0       htmlwidgets_1.5.4      grid_4.2.1            
  [7] Rtsne_0.16             munsell_0.5.0          codetools_0.2-18      
 [10] ica_1.0-3              future_1.29.0          miniUI_0.1.1.1        
 [13] withr_2.5.0            spatstat.random_3.0-1  colorspace_2.0-3      
 [16] progressr_0.11.0       highr_0.9              knitr_1.41            
 [19] rstudioapi_0.14        ROCR_1.0-11            ggsignif_0.6.4        
 [22] tensor_1.5             listenv_0.8.0          labeling_0.4.2        
 [25] git2r_0.30.1           GenomeInfoDbData_1.2.8 polyclip_1.10-4       
 [28] bit64_4.0.5            farver_2.1.1           rprojroot_2.0.3       
 [31] parallelly_1.32.1      vctrs_0.5.1            generics_0.1.3        
 [34] xfun_0.35              timechange_0.1.1       R6_2.5.1              
 [37] bitops_1.0-7           spatstat.utils_3.0-1   cachem_1.0.6          
 [40] DelayedArray_0.22.0    assertthat_0.2.1       vroom_1.6.0           
 [43] promises_1.2.0.1       scales_1.2.1           googlesheets4_1.0.1   
 [46] gtable_0.3.1           globals_0.16.2         goftest_1.2-3         
 [49] workflowr_1.7.0        rlang_1.0.6            splines_4.2.1         
 [52] rstatix_0.7.1          lazyeval_0.2.2         gargle_1.2.1          
 [55] spatstat.geom_3.0-3    broom_1.0.1            yaml_2.3.6            
 [58] reshape2_1.4.4         abind_1.4-5            modelr_0.1.10         
 [61] backports_1.4.1        httpuv_1.6.6           tools_4.2.1           
 [64] ellipsis_0.3.2         jquerylib_0.1.4        ggridges_0.5.4        
 [67] Rcpp_1.0.9             plyr_1.8.8             zlibbioc_1.42.0       
 [70] RCurl_1.98-1.9         deldir_1.0-6           pbapply_1.6-0         
 [73] cowplot_1.1.1          zoo_1.8-11             haven_2.5.1           
 [76] ggrepel_0.9.2          cluster_2.1.4          fs_1.5.2              
 [79] data.table_1.14.6      scattermore_0.8        lmtest_0.9-40         
 [82] reprex_2.0.2           RANN_2.6.1             googledrive_2.0.0     
 [85] whisker_0.4            fitdistrplus_1.1-8     hms_1.1.2             
 [88] patchwork_1.1.2        mime_0.12              evaluate_0.18         
 [91] xtable_1.8-4           readxl_1.4.1           gridExtra_2.3         
 [94] compiler_4.2.1         KernSmooth_2.23-20     crayon_1.5.2          
 [97] htmltools_0.5.3        later_1.3.0            tzdb_0.3.0            
[100] lubridate_1.9.0        DBI_1.1.3              dbplyr_2.2.1          
[103] MASS_7.3-58.1          Matrix_1.5-3           car_3.1-1             
[106] cli_3.4.1              parallel_4.2.1         igraph_1.3.5          
[109] pkgconfig_2.0.3        sp_1.5-1               plotly_4.10.1         
[112] spatstat.sparse_3.0-0  xml2_1.3.3             bslib_0.4.1           
[115] XVector_0.36.0         rvest_1.0.3            digest_0.6.30         
[118] sctransform_0.3.5      RcppAnnoy_0.0.20       spatstat.data_3.0-0   
[121] rmarkdown_2.18         cellranger_1.1.0       leiden_0.4.3          
[124] uwot_0.1.14            shiny_1.7.3            lifecycle_1.0.3       
[127] nlme_3.1-160           jsonlite_1.8.3         carData_3.0-5         
[130] fansi_1.0.3            pillar_1.8.1           lattice_0.20-45       
[133] fastmap_1.1.0          httr_1.4.4             survival_3.4-0        
[136] glue_1.6.2             png_0.1-8              bit_4.0.5             
[139] stringi_1.7.8          sass_0.4.4             irlba_2.3.5.1         
[142] future.apply_1.10.0   

date()

[1] "Mon Dec 12 17:18:39 2022"

Session information

sessionInfo()

R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

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

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

other attached packages:
 [1] viridis_0.6.2               viridisLite_0.4.1          
 [3] RColorBrewer_1.1-3          ggpubr_0.5.0               
 [5] pheatmap_1.0.12             ggsci_2.9                  
 [7] runSeurat3_0.1.0            here_1.0.1                 
 [9] magrittr_2.0.3              SeuratObject_4.1.3         
[11] Seurat_4.3.0                forcats_0.5.2              
[13] stringr_1.5.0               dplyr_1.0.10               
[15] purrr_0.3.5                 readr_2.1.3                
[17] tidyr_1.2.1                 tibble_3.1.8               
[19] ggplot2_3.4.0               tidyverse_1.3.2            
[21] SingleCellExperiment_1.18.1 SummarizedExperiment_1.26.1
[23] Biobase_2.56.0              GenomicRanges_1.48.0       
[25] GenomeInfoDb_1.32.4         IRanges_2.30.1             
[27] S4Vectors_0.34.0            BiocGenerics_0.42.0        
[29] MatrixGenerics_1.8.1        matrixStats_0.63.0         

loaded via a namespace (and not attached):
  [1] utf8_1.2.2             spatstat.explore_3.0-5 reticulate_1.26       
  [4] tidyselect_1.2.0       htmlwidgets_1.5.4      grid_4.2.1            
  [7] Rtsne_0.16             munsell_0.5.0          codetools_0.2-18      
 [10] ica_1.0-3              future_1.29.0          miniUI_0.1.1.1        
 [13] withr_2.5.0            spatstat.random_3.0-1  colorspace_2.0-3      
 [16] progressr_0.11.0       highr_0.9              knitr_1.41            
 [19] rstudioapi_0.14        ROCR_1.0-11            ggsignif_0.6.4        
 [22] tensor_1.5             listenv_0.8.0          labeling_0.4.2        
 [25] git2r_0.30.1           GenomeInfoDbData_1.2.8 polyclip_1.10-4       
 [28] bit64_4.0.5            farver_2.1.1           rprojroot_2.0.3       
 [31] parallelly_1.32.1      vctrs_0.5.1            generics_0.1.3        
 [34] xfun_0.35              timechange_0.1.1       R6_2.5.1              
 [37] bitops_1.0-7           spatstat.utils_3.0-1   cachem_1.0.6          
 [40] DelayedArray_0.22.0    assertthat_0.2.1       vroom_1.6.0           
 [43] promises_1.2.0.1       scales_1.2.1           googlesheets4_1.0.1   
 [46] gtable_0.3.1           globals_0.16.2         goftest_1.2-3         
 [49] workflowr_1.7.0        rlang_1.0.6            splines_4.2.1         
 [52] rstatix_0.7.1          lazyeval_0.2.2         gargle_1.2.1          
 [55] spatstat.geom_3.0-3    broom_1.0.1            yaml_2.3.6            
 [58] reshape2_1.4.4         abind_1.4-5            modelr_0.1.10         
 [61] backports_1.4.1        httpuv_1.6.6           tools_4.2.1           
 [64] ellipsis_0.3.2         jquerylib_0.1.4        ggridges_0.5.4        
 [67] Rcpp_1.0.9             plyr_1.8.8             zlibbioc_1.42.0       
 [70] RCurl_1.98-1.9         deldir_1.0-6           pbapply_1.6-0         
 [73] cowplot_1.1.1          zoo_1.8-11             haven_2.5.1           
 [76] ggrepel_0.9.2          cluster_2.1.4          fs_1.5.2              
 [79] data.table_1.14.6      scattermore_0.8        lmtest_0.9-40         
 [82] reprex_2.0.2           RANN_2.6.1             googledrive_2.0.0     
 [85] whisker_0.4            fitdistrplus_1.1-8     hms_1.1.2             
 [88] patchwork_1.1.2        mime_0.12              evaluate_0.18         
 [91] xtable_1.8-4           readxl_1.4.1           gridExtra_2.3         
 [94] compiler_4.2.1         KernSmooth_2.23-20     crayon_1.5.2          
 [97] htmltools_0.5.3        later_1.3.0            tzdb_0.3.0            
[100] lubridate_1.9.0        DBI_1.1.3              dbplyr_2.2.1          
[103] MASS_7.3-58.1          Matrix_1.5-3           car_3.1-1             
[106] cli_3.4.1              parallel_4.2.1         igraph_1.3.5          
[109] pkgconfig_2.0.3        sp_1.5-1               plotly_4.10.1         
[112] spatstat.sparse_3.0-0  xml2_1.3.3             bslib_0.4.1           
[115] XVector_0.36.0         rvest_1.0.3            digest_0.6.30         
[118] sctransform_0.3.5      RcppAnnoy_0.0.20       spatstat.data_3.0-0   
[121] rmarkdown_2.18         cellranger_1.1.0       leiden_0.4.3          
[124] uwot_0.1.14            shiny_1.7.3            lifecycle_1.0.3       
[127] nlme_3.1-160           jsonlite_1.8.3         carData_3.0-5         
[130] fansi_1.0.3            pillar_1.8.1           lattice_0.20-45       
[133] fastmap_1.1.0          httr_1.4.4             survival_3.4-0        
[136] glue_1.6.2             png_0.1-8              bit_4.0.5             
[139] stringi_1.7.8          sass_0.4.4             irlba_2.3.5.1         
[142] future.apply_1.10.0