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Last updated: 2022-07-27

Checks: 6 1

Knit directory: humanCardiacFibroblasts/

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

File	Version	Author	Date	Message
Rmd	c5d06fc	mluetge	2022-07-22	vis sel DE genes groups
html	c5d06fc	mluetge	2022-07-22	vis sel DE genes groups

---

load packages

suppressPackageStartupMessages({
  library(SingleCellExperiment)
  library(tidyverse)
  library(Seurat)
  library(magrittr)
  library(dplyr)
  library(purrr)
  library(ggplot2)
  library(here)
  library(runSeurat3)
  library(ggsci)
  library(ggpubr)
  library(pheatmap)
  library(viridis)
  library(sctransform)
  library(fgsea)
  library(grid)
  library(gridExtra)
  library(clusterProfiler)
  library(org.Hs.eg.db)
  library(DOSE)
  library(enrichplot)
  library(msigdbr)

})

sign plot funct

## random plotting order
shuf <- function(df){
  return(df[sample(1:dim(df)[1], dim(df)[1]),])
}

## 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(shuf(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, alpha = 0.7,
                              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
}

integrate data

basedir <- here()
seurat <- readRDS(file = paste0(basedir, 
                              "/data/humanHeartsPlusGraz_intPatients_merged", 
                              "labeled_groups_seurat.rds"))

color vectors

colPal <- pal_igv()(length(levels(seurat)))
colTec <- pal_jama()(length(unique(seurat$technique)))
colSmp <- c(pal_uchicago()(8), pal_npg()(8), pal_aaas()(10))[1:length(unique(seurat$dataset))]
colCond <- pal_npg()(length(unique(seurat$cond2)))
colID <- c(pal_jco()(10), pal_npg()(10))[1:length(unique(seurat$ID))]
colOrig <- pal_aaas()(length(unique(seurat$origin)))
colIso <- pal_nejm()(length(unique(seurat$isolation)))
colProc <- pal_aaas()(length(unique(seurat$processing)))
colLab <- pal_futurama()(length(unique(seurat$label)))

names(colPal) <- levels(seurat)
names(colTec) <- unique(seurat$technique)
names(colSmp) <- unique(seurat$dataset)
names(colCond) <- unique(seurat$cond2)
names(colID) <- unique(seurat$ID)
names(colOrig) <- unique(seurat$origin)
names(colIso) <- unique(seurat$isolation)
names(colProc) <- unique(seurat$processing)
names(colLab) <- unique(seurat$label)

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"

Version	Author	Date
c5d06fc	mluetge	2022-07-22

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")

Past versions of "vis label-1.png"

Version	Author	Date
c5d06fc	mluetge	2022-07-22

technique

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

Past versions of "vis tecnique-1.png"

Version	Author	Date
c5d06fc	mluetge	2022-07-22

Sample

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

Past versions of "vis sample-1.png"

Version	Author	Date
c5d06fc	mluetge	2022-07-22

ID

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

Version	Author	Date
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Origin

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

Past versions of "vis origin-1.png"

Version	Author	Date
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isolation

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

Past versions of "vis isolation-1.png"

Version	Author	Date
c5d06fc	mluetge	2022-07-22

cond

DimPlot(seurat, reduction = "umap", group.by = "cond2", 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"

Version	Author	Date
c5d06fc	mluetge	2022-07-22

processing

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

Past versions of "vis proc-1.png"

Version	Author	Date
c5d06fc	mluetge	2022-07-22

vis sel genes

genes <- data.frame(gene=rownames(seurat)) %>%
  mutate(geneID = gsub(".*\\.", "", gene))
selGenes <- read_tsv(paste0(basedir, "/data/selDEgenesGroups.txt")) %>% 
  left_join(., genes, by = "geneID")

seurat$label_plus_cond2 <- paste0(seurat$label, "_", seurat$cond2)
seurat$label_plus_cond2 <- as.factor(seurat$label_plus_cond2)
Idents(seurat) <- seurat$label_plus_cond2

gapVecCol <- seq(3, length(levels(seurat$label_plus_cond2)), by=3)

## keep gene order
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenes,
                    colVecIdent = colLab, colVecCond=colCond,
                    ordVec=levels(seurat$label_plus_cond2),
                    gapVecR=NULL, gapVecC=gapVecCol,cc=FALSE,
                    cr=F, condCol=T)

Past versions of "vis sel genes-1.png"

Version	Author	Date
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## cluster genes
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenes,
                    colVecIdent = colLab, colVecCond=colCond,
                    ordVec=levels(seurat$label_plus_cond2),
                    gapVecR=NULL, gapVecC=gapVecCol,cc=FALSE,
                    cr=T, condCol=T)

Past versions of "vis sel genes-2.png"

Version	Author	Date
c5d06fc	mluetge	2022-07-22

overall DE genes

Idents(seurat) <- seurat$cond2
DEgenes <-FindAllMarkers(seurat, only.pos=T, logfc.threshold = 0.1,
                           min.pct = 0.01)


clVec <- unique(seurat$cond2)
GOcons <- lapply(clVec, function(cl){
  clustDE_DatSub <- DEgenes[which(DEgenes$cluster == cl),] %>% 
    mutate(ENS=gsub("\\..*$", "", gene)) #%>% 
    #slice_min(., max_pval, n=200)
  egoSS <- enrichGO(gene      = unique(clustDE_DatSub$ENS),
                OrgDb         = org.Hs.eg.db,
                keyType       = 'ENSEMBL',
                ont           = "BP",
                pAdjustMethod = "BH",
                pvalueCutoff  = 0.05,
                qvalueCutoff  = 0.05)
  egoSS <- setReadable(egoSS, OrgDb = org.Hs.eg.db)
  egoSSres <- egoSS@result %>% filter(p.adjust < 0.05) %>% 
    mutate(subset=cl)
})

names(GOcons) <- clVec

## table to select pathways
GOconsDat <- do.call("rbind", GOcons)

selGO <- read_tsv(paste0(basedir,"/data/GSEA/selGO_overall.txt")) %>% 
  mutate(GO_cond = paste0(GOterm, "_", cond2))
GOconsDatSel <- GOconsDat %>% mutate(GO_cond = paste0(ID, "_", subset)) %>% 
  filter(GO_cond %in% selGO$GO_cond) %>% 
  mutate(cond2 = gsub(".*_", "", subset)) 

grpVec <- unique(selGO$cond2)
lapply(grpVec, function(grp){
  selGODat <- GOconsDatSel %>% filter(cond2 == grp)
  selGODat <- selGODat %>% mutate(qscore=-log(p.adjust, base=10)) 
  p <- ggbarplot(selGODat, x = "Description", y = "qscore",
          fill = "cond2",               
          color = "cond2",            
          palette = colCond,            
          sort.val = "asc",           
          sort.by.groups = TRUE      
          #x.text.angle = 90           
          ) + 
  rotate()
p
})

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signatures viridis

split by grp

signDat <- read_delim(file = paste0(basedir,
                    "/data/SelSignaturesTreat2.txt"),
                    delim = "\t")
genes <- data.frame(geneID=rownames(seurat)) %>% 
  mutate(gene=gsub("^.*\\.", "", geneID))
signDat <- signDat %>% left_join(.,genes, by="gene")
allSign <- unique(signDat$signature)

DefaultAssay(object = seurat) <- "integrated"
sce2 <- as.SingleCellExperiment(seurat)

DefaultAssay(object = seurat) <- "RNA"
sce <- as.SingleCellExperiment(seurat)
reducedDims(sce) <- list(PCA=reducedDim(sce2, "PCA"),
                         TSNE=reducedDim(sce2, "TSNE"),
                         UMAP=reducedDim(sce2, "UMAP"))

treatGrps <- unique(sce$cond2)

cutOff <- 3
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$cond2 == 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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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$cond2 == 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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cutOff <- 1
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$cond2 == 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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signatures red blue

split by grp

cutOff <- 3
pal = colorRampPalette(c("#053061", "#f7f7f7","#85122d"))(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$cond2 == 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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cutOff <- 2
pal = colorRampPalette(c("#053061", "#f7f7f7","#85122d"))(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$cond2 == 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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cutOff <- 1
pal = colorRampPalette(c("#053061", "#f7f7f7","#85122d"))(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$cond2 == 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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vis sel genes violin

genesDat <- data.frame(EnsID=rownames(seurat)) %>% 
  mutate(gene=gsub(".*\\.", "", EnsID))
selGenes <- data.frame(gene=c("BMP2", "BMP4", "BMPR1A", "BMPR2")) %>% 
  left_join(., genesDat, by="gene")

## subsample to equal number
Idents(seurat) <- seurat$cond2
seuratSub <- subset(seurat, downsample = min(table(seurat$cond2)))

pList <- sapply(selGenes$EnsID, function(x){
  p <- VlnPlot(object = seuratSub, features = x,
               group.by = "cond2",
               cols = colCond, pt.size = 0.2
               )
  plot(p)
})

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pList <- sapply(selGenes$EnsID, function(x){
  p <- VlnPlot(object = seurat, features = x,
               group.by = "label", split.by = "cond2",
               cols = colCond, pt.size = 0.1
               )
  plot(p)
})

vis sel genes avg heatmap

selGenesHM <- selGenes %>% mutate(gene = EnsID)
Idents(seurat) <- seurat$label_plus_cond2
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenesHM,
                    colVecIdent = colLab, colVecCond=colCond,
                    ordVec=levels(seurat$label_plus_cond2),
                    gapVecR=NULL, gapVecC=gapVecCol,cc=FALSE,
                    cr=T, condCol=T)

Idents(seurat) <- seurat$cond2
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenesHM,
                    colVecIdent = colCond, 
                    ordVec=levels(seurat),
                    gapVecR=NULL, gapVecC=NULL,cc=FALSE,
                    cr=T, condCol=F)

vis sel genes dotplot across label

Idents(seurat) <- seurat$label_plus_cond2
DotPlot(seurat, assay="RNA", features = selGenes$EnsID, scale =T,
        cluster.idents = T, dot.min = 0, dot.scale = 3, scale.by = "size") +
  scale_color_viridis_c() +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_discrete(breaks=selGenes$EnsID, labels=selGenes$gene) +
  xlab("") + ylab("")

vis sel genes dotplot total

Idents(seurat) <- seurat$cond2
DotPlot(seurat, assay="RNA", features = selGenes$EnsID, scale =T,
        cluster.idents = T, dot.min = 0, dot.scale = 3, scale.by = "size") +
  scale_color_viridis_c() +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_discrete(breaks=selGenes$EnsID, labels=selGenes$gene) +
  xlab("") + ylab("")

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] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] msigdbr_7.5.1               enrichplot_1.16.1          
 [3] DOSE_3.22.0                 org.Hs.eg.db_3.15.0        
 [5] AnnotationDbi_1.58.0        clusterProfiler_4.4.4      
 [7] gridExtra_2.3               fgsea_1.22.0               
 [9] sctransform_0.3.3           viridis_0.6.2              
[11] viridisLite_0.4.0           pheatmap_1.0.12            
[13] ggpubr_0.4.0                ggsci_2.9                  
[15] runSeurat3_0.1.0            here_1.0.1                 
[17] magrittr_2.0.3              sp_1.5-0                   
[19] SeuratObject_4.1.0          Seurat_4.1.1               
[21] forcats_0.5.1               stringr_1.4.0              
[23] dplyr_1.0.9                 purrr_0.3.4                
[25] readr_2.1.2                 tidyr_1.2.0                
[27] tibble_3.1.8                ggplot2_3.3.6              
[29] tidyverse_1.3.2             SingleCellExperiment_1.18.0
[31] SummarizedExperiment_1.26.1 Biobase_2.56.0             
[33] GenomicRanges_1.48.0        GenomeInfoDb_1.32.2        
[35] IRanges_2.30.0              S4Vectors_0.34.0           
[37] BiocGenerics_0.42.0         MatrixGenerics_1.8.1       
[39] matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] scattermore_0.8        bit64_4.0.5            knitr_1.39            
  [4] irlba_2.3.5            DelayedArray_0.22.0    data.table_1.14.2     
  [7] rpart_4.1.16           KEGGREST_1.36.3        RCurl_1.98-1.7        
 [10] generics_0.1.3         cowplot_1.1.1          RSQLite_2.2.15        
 [13] shadowtext_0.1.2       RANN_2.6.1             future_1.27.0         
 [16] bit_4.0.4              tzdb_0.3.0             spatstat.data_2.2-0   
 [19] xml2_1.3.3             lubridate_1.8.0        httpuv_1.6.5          
 [22] assertthat_0.2.1       gargle_1.2.0           xfun_0.31             
 [25] hms_1.1.1              jquerylib_0.1.4        babelgene_22.3        
 [28] evaluate_0.15          promises_1.2.0.1       fansi_1.0.3           
 [31] dbplyr_2.2.1           readxl_1.4.0           igraph_1.3.4          
 [34] DBI_1.1.3              htmlwidgets_1.5.4      spatstat.geom_2.4-0   
 [37] googledrive_2.0.0      ellipsis_0.3.2         backports_1.4.1       
 [40] deldir_1.0-6           vctrs_0.4.1            ROCR_1.0-11           
 [43] abind_1.4-5            cachem_1.0.6           withr_2.5.0           
 [46] ggforce_0.3.3          progressr_0.10.1       vroom_1.5.7           
 [49] treeio_1.20.1          goftest_1.2-3          cluster_2.1.3         
 [52] ape_5.6-2              lazyeval_0.2.2         crayon_1.5.1          
 [55] labeling_0.4.2         pkgconfig_2.0.3        tweenr_1.0.2          
 [58] nlme_3.1-158           rlang_1.0.4            globals_0.15.1        
 [61] lifecycle_1.0.1        miniUI_0.1.1.1         downloader_0.4        
 [64] modelr_0.1.8           cellranger_1.1.0       rprojroot_2.0.3       
 [67] polyclip_1.10-0        lmtest_0.9-40          Matrix_1.4-1          
 [70] aplot_0.1.6            carData_3.0-5          zoo_1.8-10            
 [73] reprex_2.0.1           whisker_0.4            ggridges_0.5.3        
 [76] googlesheets4_1.0.0    png_0.1-7              bitops_1.0-7          
 [79] KernSmooth_2.23-20     Biostrings_2.64.0      blob_1.2.3            
 [82] workflowr_1.7.0        qvalue_2.28.0          parallelly_1.32.1     
 [85] spatstat.random_2.2-0  rstatix_0.7.0          gridGraphics_0.5-1    
 [88] ggsignif_0.6.3         scales_1.2.0           memoise_2.0.1         
 [91] plyr_1.8.7             ica_1.0-3              zlibbioc_1.42.0       
 [94] scatterpie_0.1.7       compiler_4.2.1         RColorBrewer_1.1-3    
 [97] fitdistrplus_1.1-8     cli_3.3.0              XVector_0.36.0        
[100] listenv_0.8.0          patchwork_1.1.1        pbapply_1.5-0         
[103] MASS_7.3-58            mgcv_1.8-40            tidyselect_1.1.2      
[106] stringi_1.7.8          highr_0.9              yaml_2.3.5            
[109] GOSemSim_2.22.0        ggrepel_0.9.1          sass_0.4.2            
[112] fastmatch_1.1-3        tools_4.2.1            future.apply_1.9.0    
[115] parallel_4.2.1         rstudioapi_0.13        git2r_0.30.1          
[118] farver_2.1.1           Rtsne_0.16             ggraph_2.0.5          
[121] digest_0.6.29          rgeos_0.5-9            shiny_1.7.2           
[124] Rcpp_1.0.9             car_3.1-0              broom_1.0.0           
[127] later_1.3.0            RcppAnnoy_0.0.19       httr_1.4.3            
[130] colorspace_2.0-3       rvest_1.0.2            fs_1.5.2              
[133] tensor_1.5             reticulate_1.25        splines_4.2.1         
[136] uwot_0.1.11            yulab.utils_0.0.5      tidytree_0.3.9        
[139] spatstat.utils_2.3-1   graphlayouts_0.8.0     ggplotify_0.1.0       
[142] plotly_4.10.0          xtable_1.8-4           jsonlite_1.8.0        
[145] ggtree_3.4.1           tidygraph_1.2.1        ggfun_0.0.6           
[148] R6_2.5.1               pillar_1.8.0           htmltools_0.5.3       
[151] mime_0.12              glue_1.6.2             fastmap_1.1.0         
[154] BiocParallel_1.30.3    codetools_0.2-18       utf8_1.2.2            
[157] lattice_0.20-45        bslib_0.4.0            spatstat.sparse_2.1-1 
[160] leiden_0.4.2           GO.db_3.15.0           survival_3.3-1        
[163] rmarkdown_2.14         munsell_0.5.0          DO.db_2.9             
[166] GenomeInfoDbData_1.2.8 haven_2.5.0            reshape2_1.4.4        
[169] gtable_0.3.0           spatstat.core_2.4-4   

date()

[1] "Wed Jul 27 10:04:45 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] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] msigdbr_7.5.1               enrichplot_1.16.1          
 [3] DOSE_3.22.0                 org.Hs.eg.db_3.15.0        
 [5] AnnotationDbi_1.58.0        clusterProfiler_4.4.4      
 [7] gridExtra_2.3               fgsea_1.22.0               
 [9] sctransform_0.3.3           viridis_0.6.2              
[11] viridisLite_0.4.0           pheatmap_1.0.12            
[13] ggpubr_0.4.0                ggsci_2.9                  
[15] runSeurat3_0.1.0            here_1.0.1                 
[17] magrittr_2.0.3              sp_1.5-0                   
[19] SeuratObject_4.1.0          Seurat_4.1.1               
[21] forcats_0.5.1               stringr_1.4.0              
[23] dplyr_1.0.9                 purrr_0.3.4                
[25] readr_2.1.2                 tidyr_1.2.0                
[27] tibble_3.1.8                ggplot2_3.3.6              
[29] tidyverse_1.3.2             SingleCellExperiment_1.18.0
[31] SummarizedExperiment_1.26.1 Biobase_2.56.0             
[33] GenomicRanges_1.48.0        GenomeInfoDb_1.32.2        
[35] IRanges_2.30.0              S4Vectors_0.34.0           
[37] BiocGenerics_0.42.0         MatrixGenerics_1.8.1       
[39] matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] scattermore_0.8        bit64_4.0.5            knitr_1.39            
  [4] irlba_2.3.5            DelayedArray_0.22.0    data.table_1.14.2     
  [7] rpart_4.1.16           KEGGREST_1.36.3        RCurl_1.98-1.7        
 [10] generics_0.1.3         cowplot_1.1.1          RSQLite_2.2.15        
 [13] shadowtext_0.1.2       RANN_2.6.1             future_1.27.0         
 [16] bit_4.0.4              tzdb_0.3.0             spatstat.data_2.2-0   
 [19] xml2_1.3.3             lubridate_1.8.0        httpuv_1.6.5          
 [22] assertthat_0.2.1       gargle_1.2.0           xfun_0.31             
 [25] hms_1.1.1              jquerylib_0.1.4        babelgene_22.3        
 [28] evaluate_0.15          promises_1.2.0.1       fansi_1.0.3           
 [31] dbplyr_2.2.1           readxl_1.4.0           igraph_1.3.4          
 [34] DBI_1.1.3              htmlwidgets_1.5.4      spatstat.geom_2.4-0   
 [37] googledrive_2.0.0      ellipsis_0.3.2         backports_1.4.1       
 [40] deldir_1.0-6           vctrs_0.4.1            ROCR_1.0-11           
 [43] abind_1.4-5            cachem_1.0.6           withr_2.5.0           
 [46] ggforce_0.3.3          progressr_0.10.1       vroom_1.5.7           
 [49] treeio_1.20.1          goftest_1.2-3          cluster_2.1.3         
 [52] ape_5.6-2              lazyeval_0.2.2         crayon_1.5.1          
 [55] labeling_0.4.2         pkgconfig_2.0.3        tweenr_1.0.2          
 [58] nlme_3.1-158           rlang_1.0.4            globals_0.15.1        
 [61] lifecycle_1.0.1        miniUI_0.1.1.1         downloader_0.4        
 [64] modelr_0.1.8           cellranger_1.1.0       rprojroot_2.0.3       
 [67] polyclip_1.10-0        lmtest_0.9-40          Matrix_1.4-1          
 [70] aplot_0.1.6            carData_3.0-5          zoo_1.8-10            
 [73] reprex_2.0.1           whisker_0.4            ggridges_0.5.3        
 [76] googlesheets4_1.0.0    png_0.1-7              bitops_1.0-7          
 [79] KernSmooth_2.23-20     Biostrings_2.64.0      blob_1.2.3            
 [82] workflowr_1.7.0        qvalue_2.28.0          parallelly_1.32.1     
 [85] spatstat.random_2.2-0  rstatix_0.7.0          gridGraphics_0.5-1    
 [88] ggsignif_0.6.3         scales_1.2.0           memoise_2.0.1         
 [91] plyr_1.8.7             ica_1.0-3              zlibbioc_1.42.0       
 [94] scatterpie_0.1.7       compiler_4.2.1         RColorBrewer_1.1-3    
 [97] fitdistrplus_1.1-8     cli_3.3.0              XVector_0.36.0        
[100] listenv_0.8.0          patchwork_1.1.1        pbapply_1.5-0         
[103] MASS_7.3-58            mgcv_1.8-40            tidyselect_1.1.2      
[106] stringi_1.7.8          highr_0.9              yaml_2.3.5            
[109] GOSemSim_2.22.0        ggrepel_0.9.1          sass_0.4.2            
[112] fastmatch_1.1-3        tools_4.2.1            future.apply_1.9.0    
[115] parallel_4.2.1         rstudioapi_0.13        git2r_0.30.1          
[118] farver_2.1.1           Rtsne_0.16             ggraph_2.0.5          
[121] digest_0.6.29          rgeos_0.5-9            shiny_1.7.2           
[124] Rcpp_1.0.9             car_3.1-0              broom_1.0.0           
[127] later_1.3.0            RcppAnnoy_0.0.19       httr_1.4.3            
[130] colorspace_2.0-3       rvest_1.0.2            fs_1.5.2              
[133] tensor_1.5             reticulate_1.25        splines_4.2.1         
[136] uwot_0.1.11            yulab.utils_0.0.5      tidytree_0.3.9        
[139] spatstat.utils_2.3-1   graphlayouts_0.8.0     ggplotify_0.1.0       
[142] plotly_4.10.0          xtable_1.8-4           jsonlite_1.8.0        
[145] ggtree_3.4.1           tidygraph_1.2.1        ggfun_0.0.6           
[148] R6_2.5.1               pillar_1.8.0           htmltools_0.5.3       
[151] mime_0.12              glue_1.6.2             fastmap_1.1.0         
[154] BiocParallel_1.30.3    codetools_0.2-18       utf8_1.2.2            
[157] lattice_0.20-45        bslib_0.4.0            spatstat.sparse_2.1-1 
[160] leiden_0.4.2           GO.db_3.15.0           survival_3.3-1        
[163] rmarkdown_2.14         munsell_0.5.0          DO.db_2.9             
[166] GenomeInfoDbData_1.2.8 haven_2.5.0            reshape2_1.4.4        
[169] gtable_0.3.0           spatstat.core_2.4-4