Merge samples
Mechthild Lütge
14 May 2020
Last updated: 2022-07-27
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$group_var)) {
t_group <- t_group + scale_color_viridis(option = "D")
}
else {
t_group <- t_group + scale_color_manual(values = col_group)
}
t_group
}integrate data
basedir <- here()
seurat <- readRDS(file = paste0(basedir,
"/data/humanHeartsPlusGraz_intPatients_merged",
"labeled_groups_seurat.rds"))
seurat <- subset(seurat, cond2 == "MyocarditisLT", invert=T)color vectors
colPal <- pal_igv()(length(levels(seurat)))
colTec <- pal_jama()(length(unique(seurat$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")
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$label_plus_cond2
gapVecCol <- seq(2, length(levels(seurat$label_plus_cond2)), by=2)
## keep gene order
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenes,
colVecIdent = colLab, colVecCond=colCond,
ordVec=levels(seurat$label_plus_cond2),
gapVecR=NULL, gapVecC=gapVecCol,cc=FALSE,
cr=F, condCol=T)
## cluster genes
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenes,
colVecIdent = colLab, colVecCond=colCond,
ordVec=levels(seurat$label_plus_cond2),
gapVecR=NULL, gapVecC=gapVecCol,cc=FALSE,
cr=T, condCol=T)
## 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$cond2)
lapply(grpVec, function(grp){
selGODat <- GOconsDatSel %>% filter(cond2 == grp)
selGODat <- selGODat %>% mutate(qscore=-log(p.adjust, base=10))
p <- ggbarplot(selGODat, x = "Description", y = "qscore",
fill = "cond2",
color = "cond2",
palette = colCond,
sort.val = "asc",
sort.by.groups = TRUE
#x.text.angle = 90
) +
rotate()
p
})[[1]]
[[2]]
[[3]]
signatures viridis
split by grp
signDat <- read_delim(file = paste0(basedir,
"/data/SelSignaturesTreat2.txt"),
delim = "\t")
genes <- data.frame(geneID=rownames(seurat)) %>%
mutate(gene=gsub("^.*\\.", "", geneID))
signDat <- signDat %>% left_join(.,genes, by="gene")
allSign <- unique(signDat$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$cond2 == treat)]
p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
sc +
guides(colour = guide_colourbar(title = '')) +
ggtitle(paste0(sign, ' signature - ', treat)) +
theme_classic() +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(x='Dimension 1', y='Dimension 2')
p
})
})[[1]]
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cutOff <- 1
pal = viridis(100)
sc <- scale_colour_gradientn(colours = pal, limits=c(0, cutOff))
lapply(unique(signDat$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
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$cond2 == treat)]
p <- visGroup_adapt(sceSubT, 'sign', dim_red = 'UMAP') +
sc +
guides(colour = guide_colourbar(title = '')) +
ggtitle(paste0(sign, ' signature - ', treat)) +
theme_classic() +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(x='Dimension 1', y='Dimension 2')
p
})
})[[1]]
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vis sel genes violin
genesDat <- data.frame(EnsID=rownames(seurat)) %>%
mutate(gene=gsub(".*\\.", "", EnsID))
selGenes <- data.frame(gene=c("BMP2", "BMP4", "BMPR1A", "BMPR2")) %>%
left_join(., genesDat, by="gene")
## subsample to equal number
Idents(seurat) <- seurat$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)
})
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 =F,
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 ggridges_0.5.3 googlesheets4_1.0.0
[76] png_0.1-7 bitops_1.0-7 KernSmooth_2.23-20
[79] Biostrings_2.64.0 blob_1.2.3 workflowr_1.7.0
[82] qvalue_2.28.0 parallelly_1.32.1 spatstat.random_2.2-0
[85] rstatix_0.7.0 gridGraphics_0.5-1 ggsignif_0.6.3
[88] scales_1.2.0 memoise_2.0.1 plyr_1.8.7
[91] ica_1.0-3 zlibbioc_1.42.0 compiler_4.2.1
[94] scatterpie_0.1.7 RColorBrewer_1.1-3 fitdistrplus_1.1-8
[97] cli_3.3.0 XVector_0.36.0 listenv_0.8.0
[100] patchwork_1.1.1 pbapply_1.5-0 MASS_7.3-58
[103] mgcv_1.8-40 tidyselect_1.1.2 stringi_1.7.8
[106] highr_0.9 yaml_2.3.5 GOSemSim_2.22.0
[109] ggrepel_0.9.1 sass_0.4.2 fastmatch_1.1-3
[112] tools_4.2.1 future.apply_1.9.0 parallel_4.2.1
[115] rstudioapi_0.13 git2r_0.30.1 farver_2.1.1
[118] Rtsne_0.16 ggraph_2.0.5 digest_0.6.29
[121] rgeos_0.5-9 shiny_1.7.2 Rcpp_1.0.9
[124] car_3.1-0 broom_1.0.0 later_1.3.0
[127] RcppAnnoy_0.0.19 httr_1.4.3 colorspace_2.0-3
[130] rvest_1.0.2 fs_1.5.2 tensor_1.5
[133] reticulate_1.25 splines_4.2.1 uwot_0.1.11
[136] yulab.utils_0.0.5 tidytree_0.3.9 spatstat.utils_2.3-1
[139] graphlayouts_0.8.0 ggplotify_0.1.0 plotly_4.10.0
[142] xtable_1.8-4 jsonlite_1.8.0 ggtree_3.4.1
[145] tidygraph_1.2.1 ggfun_0.0.6 R6_2.5.1
[148] pillar_1.8.0 htmltools_0.5.3 mime_0.12
[151] glue_1.6.2 fastmap_1.1.0 BiocParallel_1.30.3
[154] codetools_0.2-18 utf8_1.2.2 lattice_0.20-45
[157] bslib_0.4.0 spatstat.sparse_2.1-1 leiden_0.4.2
[160] GO.db_3.15.0 survival_3.3-1 rmarkdown_2.14
[163] munsell_0.5.0 DO.db_2.9 GenomeInfoDbData_1.2.8
[166] haven_2.5.0 reshape2_1.4.4 gtable_0.3.0
[169] spatstat.core_2.4-4 date()[1] "Wed Jul 27 17:33:49 2022"
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 ggridges_0.5.3 googlesheets4_1.0.0
[76] png_0.1-7 bitops_1.0-7 KernSmooth_2.23-20
[79] Biostrings_2.64.0 blob_1.2.3 workflowr_1.7.0
[82] qvalue_2.28.0 parallelly_1.32.1 spatstat.random_2.2-0
[85] rstatix_0.7.0 gridGraphics_0.5-1 ggsignif_0.6.3
[88] scales_1.2.0 memoise_2.0.1 plyr_1.8.7
[91] ica_1.0-3 zlibbioc_1.42.0 compiler_4.2.1
[94] scatterpie_0.1.7 RColorBrewer_1.1-3 fitdistrplus_1.1-8
[97] cli_3.3.0 XVector_0.36.0 listenv_0.8.0
[100] patchwork_1.1.1 pbapply_1.5-0 MASS_7.3-58
[103] mgcv_1.8-40 tidyselect_1.1.2 stringi_1.7.8
[106] highr_0.9 yaml_2.3.5 GOSemSim_2.22.0
[109] ggrepel_0.9.1 sass_0.4.2 fastmatch_1.1-3
[112] tools_4.2.1 future.apply_1.9.0 parallel_4.2.1
[115] rstudioapi_0.13 git2r_0.30.1 farver_2.1.1
[118] Rtsne_0.16 ggraph_2.0.5 digest_0.6.29
[121] rgeos_0.5-9 shiny_1.7.2 Rcpp_1.0.9
[124] car_3.1-0 broom_1.0.0 later_1.3.0
[127] RcppAnnoy_0.0.19 httr_1.4.3 colorspace_2.0-3
[130] rvest_1.0.2 fs_1.5.2 tensor_1.5
[133] reticulate_1.25 splines_4.2.1 uwot_0.1.11
[136] yulab.utils_0.0.5 tidytree_0.3.9 spatstat.utils_2.3-1
[139] graphlayouts_0.8.0 ggplotify_0.1.0 plotly_4.10.0
[142] xtable_1.8-4 jsonlite_1.8.0 ggtree_3.4.1
[145] tidygraph_1.2.1 ggfun_0.0.6 R6_2.5.1
[148] pillar_1.8.0 htmltools_0.5.3 mime_0.12
[151] glue_1.6.2 fastmap_1.1.0 BiocParallel_1.30.3
[154] codetools_0.2-18 utf8_1.2.2 lattice_0.20-45
[157] bslib_0.4.0 spatstat.sparse_2.1-1 leiden_0.4.2
[160] GO.db_3.15.0 survival_3.3-1 rmarkdown_2.14
[163] munsell_0.5.0 DO.db_2.9 GenomeInfoDbData_1.2.8
[166] haven_2.5.0 reshape2_1.4.4 gtable_0.3.0
[169] spatstat.core_2.4-4