Merge samples
Mechthild Lütge
14 May 2020
Last updated: 2022-07-19
Checks: 6 1
Knit directory: humanCardiacFibroblasts/
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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(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$group_var)) {
t_group <- t_group + scale_color_viridis(option = "D")
}
else {
t_group <- t_group + scale_color_manual(values = col_group)
}
t_group
}integrate data
basedir <- here()
seurat <- readRDS(file = paste0(basedir,
"/data/humanHeartsPlusGraz_intPatients_merged",
"labeled_seurat.rds"))
myoGrp <- c("GZ1","GZ4","GZ6","SG29","SG32")
myoLTGrp <- c("GZ2","GZ3","GZ5","GZ7","SG31")
CtrlGrp <- c("GZ8","GZ10","GZ11","GZ12")
seurat$cond2 <- "HH"
seurat$cond2[which(seurat$ID %in% myoGrp)] <- "MyocarditisHT"
seurat$cond2[which(seurat$ID %in% myoLTGrp)] <- "MyocarditisLT"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")
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")
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")
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")
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")
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")
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")
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")
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")
overall DE genes HH vs MyocarditisLT
seurat <- subset(seurat, cond2 == "MyocarditisHT", invert=T)
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$ENS),
OrgDb = org.Hs.eg.db,
keyType = 'ENSEMBL',
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 0.05)
egoSS <- setReadable(egoSS, OrgDb = org.Hs.eg.db)
egoSSres <- egoSS@result %>% filter(p.adjust < 0.05) %>%
mutate(subset=cl)
})
names(GOcons) <- clVec
## table to select pathways
GOconsDat <- do.call("rbind", GOcons)
write.table(GOconsDat, quote=F, row.names = T, col.names = T, sep= "\t",
file = paste0(basedir,"/data/humanHeartsPlusGraz_intPatients_",
"merged_SubsetLT_overallDEGO.txt"))cw DE genes
Idents(seurat) <- seurat$cond2
grpVec <- unique(seurat$label)
clustDE <- lapply(grpVec, function(grp){
grpSub <- unique(seurat$label)[which(
unique(seurat$label)==grp)]
seuratSub <- subset(seurat, label == grpSub)
DEgenes <-FindAllMarkers(seuratSub, only.pos=T, logfc.threshold = 0.1,
min.pct = 0.01)
if(nrow(DEgenes)>1){
DEgenes <- DEgenes %>% filter(p_val_adj<0.01) %>%
mutate(group=paste0(grp, "_", cluster)) %>%
mutate(geneID=gsub(".*\\.", "", gene)) %>%
filter(nchar(geneID)>1)
}
})
names(clustDE) <- grpVec
clustDE_Dat <- data.frame(do.call("rbind", clustDE))
write.table(clustDE_Dat,
file=paste0(basedir,
"/data/humanHeartsPlusGraz_intPatients_",
"merged_SubsetLT_cwDEGenes.txt"),
row.names = FALSE, col.names = TRUE, quote = FALSE, sep = "\t")avg Heatmap top cwDE genes
other
Endothelial
Fibroblast
Perivascular
Cardiomyocyte
MonocyteMacrophage
Tcell
GSEA clusterProfiler cw DEgenes
#clVec <- levels(seurat$label_plus_cond2)
## only include cluster with >1 DE gene
clInclude <- data.frame(table(clustDE_Dat$group)) %>% filter(Freq>1)
clVec <- clInclude$Var1
GOcons <- lapply(clVec, function(cl){
clustDE_DatSub <- clustDE_Dat[which(clustDE_Dat$group == cl),] %>%
mutate(ENS=gsub("\\..*$", "", gene)) #%>%
#slice_min(., max_pval, n=200)
egoSS <- enrichGO(gene = unique(clustDE_DatSub$ENS),
OrgDb = org.Hs.eg.db,
keyType = 'ENSEMBL',
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 0.05)
egoSS <- setReadable(egoSS, OrgDb = org.Hs.eg.db)
egoSSres <- egoSS@result %>% filter(p.adjust < 0.05) %>%
mutate(subset=cl)
})
names(GOcons) <- clVec
## table to select pathways
GOconsDat <- do.call("rbind", GOcons)
write.table(GOconsDat, quote=F, row.names = T, col.names = T, sep= "\t",
file = paste0(basedir,"/data/humanHeartsPlusGraz_intPatients_",
"merged_SubsetLT_cwDEGO.txt"))vis sel GO Fibroblasts
selGO <- read_tsv(paste0(basedir,"/data/GSEA/selGO_Fibroblasts.txt"))
GOconsDatSel <- GOconsDat %>% filter(ID %in% selGO$GOterm) %>%
mutate(cond2 = gsub(".*_", "", subset)) %>%
mutate(label= gsub("_.*", "", subset)) %>%
filter(label == "Fibroblast")
grpVec <- unique(selGO$cond2)
lapply(grpVec, function(grp){
selGODat <- GOconsDatSel %>% filter(cond2 == grp)
selGODat <- selGODat %>% mutate(qscore=-log(p.adjust, base=10))
p <- ggbarplot(selGODat, x = "Description", y = "qscore",
fill = "cond2",
color = "cond2",
palette = colCond,
sort.val = "asc",
sort.by.groups = TRUE
#x.text.angle = 90
) +
rotate()
p
})[[1]]
[[2]]
vis sel GO T cells
selGO <- read_tsv(paste0(basedir,"/data/GSEA/selGO_Tcell.txt"))
GOconsDatSel <- GOconsDat %>% filter(ID %in% selGO$GOterm) %>%
mutate(cond2 = gsub(".*_", "", subset)) %>%
mutate(label= gsub("_.*", "", subset)) %>%
filter(label == "Tcell")
grpVec <- unique(selGO$cond2)
lapply(grpVec, function(grp){
selGODat <- GOconsDatSel %>% filter(cond2 == grp)
selGODat <- selGODat %>% mutate(qscore=-log(p.adjust, base=10))
p <- ggbarplot(selGODat, x = "Description", y = "qscore",
fill = "cond2",
color = "cond2",
palette = colCond,
sort.val = "asc",
sort.by.groups = TRUE
#x.text.angle = 90
) +
rotate()
p
})[[1]]
signatures viridis
split by grp
signDat <- read_delim(file = paste0(basedir,
"/data/SelSignaturesTreat2.txt"),
delim = "\t")
genes <- data.frame(geneID=rownames(seurat)) %>%
mutate(gene=gsub("^.*\\.", "", geneID))
signDat <- signDat %>% left_join(.,genes, by="gene")
allSign <- unique(signDat$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
})
})[[1]]
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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$cond2 == treat)]
p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
sc +
guides(colour = guide_colourbar(title = '')) +
ggtitle(paste0(sign, ' signature - ', treat)) +
theme_classic() +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(x='Dimension 1', y='Dimension 2')
p
})
})[[1]]
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cutOff <- 1.5
pal = viridis(100)
sc <- scale_colour_gradientn(colours = pal, limits=c(0, cutOff))
lapply(unique(signDat$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
})
})[[1]]
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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$cond2 == treat)]
p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
sc +
guides(colour = guide_colourbar(title = '')) +
ggtitle(paste0(sign, ' signature - ', treat)) +
theme_classic() +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(x='Dimension 1', y='Dimension 2')
p
})
})[[1]]
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cutOff <- 2
pal = colorRampPalette(c("#053061", "#f7f7f7","#85122d"))(100)
sc <- scale_colour_gradientn(colours = pal, limits=c(0, cutOff))
lapply(unique(signDat$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
})
})[[1]]
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cutOff <- 1.5
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$cond2 == treat)]
p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
sc +
guides(colour = guide_colourbar(title = '')) +
ggtitle(paste0(sign, ' signature - ', treat)) +
theme_classic() +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(x='Dimension 1', y='Dimension 2')
p
})
})[[1]]
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vis sel genes violin
genesDat <- data.frame(EnsID=rownames(seurat)) %>%
mutate(gene=gsub(".*\\.", "", EnsID))
selGenes <- data.frame(gene=c("BMP2", "BMP4", "BMPR1A", "BMPR2")) %>%
left_join(., genesDat, by="gene")
## subsample to equal number
seuratSub <- subset(seurat, downsample = min(table(seurat$cond2)))
pList <- sapply(selGenes$EnsID, function(x){
p <- VlnPlot(object = seuratSub, features = x,
group.by = "cond2",
cols = colCond, pt.size = 0.2
)
plot(p)
})
vis sel genes violin Fibroblasts
seuratSub <- subset(seurat, label == "Fibroblast")
## subsample to equal number
seuratSub <- subset(seuratSub, downsample = min(table(seuratSub$cond2)))
pList <- sapply(selGenes$EnsID, function(x){
p <- VlnPlot(object = seuratSub, features = x,
group.by = "cond2",
cols = colCond, pt.size = 0.2
)
plot(p)
})
session info
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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 parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] msigdbr_7.5.1 enrichplot_1.12.3
[3] DOSE_3.18.3 org.Hs.eg.db_3.13.0
[5] AnnotationDbi_1.54.1 clusterProfiler_4.0.5
[7] gridExtra_2.3 fgsea_1.18.0
[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_1.4-7
[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_3.1.7 ggplot2_3.3.6
[29] tidyverse_1.3.1 SingleCellExperiment_1.14.1
[31] SummarizedExperiment_1.22.0 Biobase_2.52.0
[33] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
[35] IRanges_2.26.0 S4Vectors_0.30.2
[37] BiocGenerics_0.38.0 MatrixGenerics_1.4.3
[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_0.18.0 data.table_1.14.2
[7] rpart_4.1.16 KEGGREST_1.32.0 RCurl_1.98-1.6
[10] generics_0.1.2 cowplot_1.1.1 RSQLite_2.2.14
[13] shadowtext_0.1.2 RANN_2.6.1 future_1.25.0
[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 xfun_0.30 hms_1.1.1
[25] jquerylib_0.1.4 babelgene_22.3 evaluate_0.15
[28] promises_1.2.0.1 fansi_1.0.3 dbplyr_2.1.1
[31] readxl_1.4.0 igraph_1.3.1 DBI_1.1.2
[34] htmlwidgets_1.5.4 spatstat.geom_2.4-0 ellipsis_0.3.2
[37] backports_1.4.1 deldir_1.0-6 vctrs_0.4.1
[40] ROCR_1.0-11 abind_1.4-5 cachem_1.0.6
[43] withr_2.5.0 ggforce_0.3.3 progressr_0.10.0
[46] vroom_1.5.7 treeio_1.16.2 goftest_1.2-3
[49] cluster_2.1.3 ape_5.6-2 lazyeval_0.2.2
[52] crayon_1.5.1 pkgconfig_2.0.3 labeling_0.4.2
[55] tweenr_1.0.2 nlme_3.1-157 rlang_1.0.2
[58] globals_0.15.0 lifecycle_1.0.1 miniUI_0.1.1.1
[61] downloader_0.4 modelr_0.1.8 cellranger_1.1.0
[64] rprojroot_2.0.3 polyclip_1.10-0 lmtest_0.9-40
[67] Matrix_1.4-1 aplot_0.1.4 carData_3.0-5
[70] zoo_1.8-10 reprex_2.0.1 ggridges_0.5.3
[73] png_0.1-7 bitops_1.0-7 KernSmooth_2.23-20
[76] Biostrings_2.60.2 blob_1.2.3 workflowr_1.7.0
[79] qvalue_2.24.0 parallelly_1.31.1 spatstat.random_2.2-0
[82] rstatix_0.7.0 gridGraphics_0.5-1 ggsignif_0.6.3
[85] scales_1.2.0 memoise_2.0.1 plyr_1.8.7
[88] ica_1.0-2 zlibbioc_1.38.0 compiler_4.1.0
[91] scatterpie_0.1.7 RColorBrewer_1.1-3 fitdistrplus_1.1-8
[94] cli_3.3.0 XVector_0.32.0 listenv_0.8.0
[97] patchwork_1.1.1 pbapply_1.5-0 MASS_7.3-57
[100] mgcv_1.8-40 tidyselect_1.1.2 stringi_1.7.6
[103] highr_0.9 yaml_2.3.5 GOSemSim_2.18.1
[106] ggrepel_0.9.1 sass_0.4.1 fastmatch_1.1-3
[109] tools_4.1.0 future.apply_1.9.0 rstudioapi_0.13
[112] git2r_0.30.1 farver_2.1.0 Rtsne_0.16
[115] ggraph_2.0.5 digest_0.6.29 rgeos_0.5-9
[118] shiny_1.7.1 Rcpp_1.0.8.3 car_3.0-13
[121] broom_0.8.0 later_1.3.0 RcppAnnoy_0.0.19
[124] httr_1.4.3 colorspace_2.0-3 rvest_1.0.2
[127] fs_1.5.2 tensor_1.5 reticulate_1.24
[130] splines_4.1.0 uwot_0.1.11 yulab.utils_0.0.4
[133] tidytree_0.3.9 spatstat.utils_2.3-1 graphlayouts_0.8.0
[136] ggplotify_0.1.0 plotly_4.10.0 xtable_1.8-4
[139] jsonlite_1.8.0 ggtree_3.0.4 tidygraph_1.2.1
[142] ggfun_0.0.6 R6_2.5.1 pillar_1.7.0
[145] htmltools_0.5.2 mime_0.12 glue_1.6.2
[148] fastmap_1.1.0 BiocParallel_1.26.2 codetools_0.2-18
[151] utf8_1.2.2 lattice_0.20-45 bslib_0.3.1
[154] spatstat.sparse_2.1-1 leiden_0.3.10 GO.db_3.13.0
[157] limma_3.48.3 survival_3.3-1 rmarkdown_2.14
[160] munsell_0.5.0 DO.db_2.9 GenomeInfoDbData_1.2.6
[163] haven_2.5.0 reshape2_1.4.4 gtable_0.3.0
[166] spatstat.core_2.4-2 date()[1] "Tue Jul 19 15:03:36 2022"
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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 parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] msigdbr_7.5.1 enrichplot_1.12.3
[3] DOSE_3.18.3 org.Hs.eg.db_3.13.0
[5] AnnotationDbi_1.54.1 clusterProfiler_4.0.5
[7] gridExtra_2.3 fgsea_1.18.0
[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_1.4-7
[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_3.1.7 ggplot2_3.3.6
[29] tidyverse_1.3.1 SingleCellExperiment_1.14.1
[31] SummarizedExperiment_1.22.0 Biobase_2.52.0
[33] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
[35] IRanges_2.26.0 S4Vectors_0.30.2
[37] BiocGenerics_0.38.0 MatrixGenerics_1.4.3
[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_0.18.0 data.table_1.14.2
[7] rpart_4.1.16 KEGGREST_1.32.0 RCurl_1.98-1.6
[10] generics_0.1.2 cowplot_1.1.1 RSQLite_2.2.14
[13] shadowtext_0.1.2 RANN_2.6.1 future_1.25.0
[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 xfun_0.30 hms_1.1.1
[25] jquerylib_0.1.4 babelgene_22.3 evaluate_0.15
[28] promises_1.2.0.1 fansi_1.0.3 dbplyr_2.1.1
[31] readxl_1.4.0 igraph_1.3.1 DBI_1.1.2
[34] htmlwidgets_1.5.4 spatstat.geom_2.4-0 ellipsis_0.3.2
[37] backports_1.4.1 deldir_1.0-6 vctrs_0.4.1
[40] ROCR_1.0-11 abind_1.4-5 cachem_1.0.6
[43] withr_2.5.0 ggforce_0.3.3 progressr_0.10.0
[46] vroom_1.5.7 treeio_1.16.2 goftest_1.2-3
[49] cluster_2.1.3 ape_5.6-2 lazyeval_0.2.2
[52] crayon_1.5.1 pkgconfig_2.0.3 labeling_0.4.2
[55] tweenr_1.0.2 nlme_3.1-157 rlang_1.0.2
[58] globals_0.15.0 lifecycle_1.0.1 miniUI_0.1.1.1
[61] downloader_0.4 modelr_0.1.8 cellranger_1.1.0
[64] rprojroot_2.0.3 polyclip_1.10-0 lmtest_0.9-40
[67] Matrix_1.4-1 aplot_0.1.4 carData_3.0-5
[70] zoo_1.8-10 reprex_2.0.1 ggridges_0.5.3
[73] png_0.1-7 bitops_1.0-7 KernSmooth_2.23-20
[76] Biostrings_2.60.2 blob_1.2.3 workflowr_1.7.0
[79] qvalue_2.24.0 parallelly_1.31.1 spatstat.random_2.2-0
[82] rstatix_0.7.0 gridGraphics_0.5-1 ggsignif_0.6.3
[85] scales_1.2.0 memoise_2.0.1 plyr_1.8.7
[88] ica_1.0-2 zlibbioc_1.38.0 compiler_4.1.0
[91] scatterpie_0.1.7 RColorBrewer_1.1-3 fitdistrplus_1.1-8
[94] cli_3.3.0 XVector_0.32.0 listenv_0.8.0
[97] patchwork_1.1.1 pbapply_1.5-0 MASS_7.3-57
[100] mgcv_1.8-40 tidyselect_1.1.2 stringi_1.7.6
[103] highr_0.9 yaml_2.3.5 GOSemSim_2.18.1
[106] ggrepel_0.9.1 sass_0.4.1 fastmatch_1.1-3
[109] tools_4.1.0 future.apply_1.9.0 rstudioapi_0.13
[112] git2r_0.30.1 farver_2.1.0 Rtsne_0.16
[115] ggraph_2.0.5 digest_0.6.29 rgeos_0.5-9
[118] shiny_1.7.1 Rcpp_1.0.8.3 car_3.0-13
[121] broom_0.8.0 later_1.3.0 RcppAnnoy_0.0.19
[124] httr_1.4.3 colorspace_2.0-3 rvest_1.0.2
[127] fs_1.5.2 tensor_1.5 reticulate_1.24
[130] splines_4.1.0 uwot_0.1.11 yulab.utils_0.0.4
[133] tidytree_0.3.9 spatstat.utils_2.3-1 graphlayouts_0.8.0
[136] ggplotify_0.1.0 plotly_4.10.0 xtable_1.8-4
[139] jsonlite_1.8.0 ggtree_3.0.4 tidygraph_1.2.1
[142] ggfun_0.0.6 R6_2.5.1 pillar_1.7.0
[145] htmltools_0.5.2 mime_0.12 glue_1.6.2
[148] fastmap_1.1.0 BiocParallel_1.26.2 codetools_0.2-18
[151] utf8_1.2.2 lattice_0.20-45 bslib_0.3.1
[154] spatstat.sparse_2.1-1 leiden_0.3.10 GO.db_3.13.0
[157] limma_3.48.3 survival_3.3-1 rmarkdown_2.14
[160] munsell_0.5.0 DO.db_2.9 GenomeInfoDbData_1.2.6
[163] haven_2.5.0 reshape2_1.4.4 gtable_0.3.0
[166] spatstat.core_2.4-2