integrate data from adult mLN and iLN
A.DeMartin
2025-05-06
Last updated: 2025-07-11
Checks: 6 1
Knit directory: LNdevMouse24.2/
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preprocessing
load packages
library(ExploreSCdataSeurat3)
library(runSeurat3)
library(Seurat)
library(ggpubr)
library(pheatmap)
library(SingleCellExperiment)
library(dplyr)
library(tidyverse)
library(viridis)
library(here)
library(muscat)
library(circlize)
library(destiny)
library(scater)
library(metap)
library(multtest)
library(clusterProfiler)
library(org.Hs.eg.db)
library(msigdbr)
library(enrichplot)
library(DOSE)
library(grid)
library(gridExtra)
library(ggupset)
library(NCmisc)load object all
basedir <- here()
fileNam <- paste0(basedir, "/data/LNmLToRev_allmerged_seurat.rds")
seuratM <- readRDS(fileNam)
table(seuratM$orig.ident)
140291 subset adult
seuratA <- subset(seuratM, timepoint == "8w")
table(seuratM$timepoint)
E18 P7 3w 8w
42711 44836 29577 23167 table(seuratA$timepoint)
8w
23167 #rerun seurat
seuratA <- NormalizeData (object = seuratA)
seuratA <- FindVariableFeatures(object = seuratA)
seuratA <- ScaleData(object = seuratA, verbose = TRUE)
seuratA <- RunPCA(object=seuratA, npcs = 30, verbose = FALSE)
seuratA <- RunTSNE(object=seuratA, reduction="pca", dims = 1:20)
seuratA <- RunUMAP(object=seuratA, reduction="pca", dims = 1:20)
seuratA <- FindNeighbors(object = seuratA, reduction = "pca", dims= 1:20)
res <- c(0.25, 0.6, 0.8, 0.4)
for (i in 1:length(res)) {
seuratA <- FindClusters(object = seuratA, resolution = res[i], random.seed = 1234)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 23167
Number of edges: 747906
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9267
Number of communities: 12
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 23167
Number of edges: 747906
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8790
Number of communities: 17
Elapsed time: 4 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 23167
Number of edges: 747906
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8611
Number of communities: 20
Elapsed time: 4 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 23167
Number of edges: 747906
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9025
Number of communities: 15
Elapsed time: 5 secondsplot umaps
clustering
colPal <- c("#DAF7A6", "#FFC300", "#FF5733", "#C70039", "#900C3F", "#b66e8d",
"#61a4ba", "#6178ba", "#54a87f", "#25328a", "#b6856e",
"#ba6161", "#20714a", "#0073C2FF", "#EFC000FF", "#868686FF",
"#CD534CFF","#7AA6DCFF", "#003C67FF", "#8F7700FF", "#3B3B3BFF",
"#A73030FF", "#4A6990FF")[1:length(unique(seuratM$RNA_snn_res.0.25))]
names(colPal) <- unique(seuratM$RNA_snn_res.0.25)
DimPlot(seuratA, reduction = "umap", group.by = "RNA_snn_res.0.25" ,
pt.size = 0.1, label = T, shuffle = T, cols = colPal) +
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("umap1") +
ylab("umap2")
location
collocation <- c("#61baba", "#ba6161")
names(collocation) <- c("iLN", "mLN")
DimPlot(seuratA, reduction = "umap", group.by = "location", cols = collocation,
pt.size = 0.1, label = T, shuffle = T) +
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("umap1") +
ylab("umap2")
calculate cluster marker genes pre filter
Idents(seuratA) <- seuratA$RNA_snn_res.0.25
levels(seuratA)
markerGenes <- FindAllMarkers(seuratA, only.pos=T) %>%
dplyr::filter(p_val_adj < 0.01) features pre filter
selGenesViolin <- c("ENSMUSG00000020717.Pecam1", "ENSMUSG00000026395.Ptprc", "ENSMUSG00000031004.Mki67")
pList <- sapply(selGenesViolin, function(x){
p <- FeaturePlot(seuratA, reduction = "umap",
features = x,
cols=c("lightgrey", "darkred"),
order = T)+
theme(legend.position="right")
plot(p)
})
filter object
## filter Pecam1 (cluster #6) and Ptprc (cluster #9 and #10) Mki67 (#8) and pancreatic cells (#11)
table(seuratA$RNA_snn_res.0.25)
seuratF <- subset(seuratA, RNA_snn_res.0.25 %in% c("6", "8" ,"9", "10", "11"), invert = TRUE)
table(seuratF$RNA_snn_res.0.25)
seuratA <- seuratF
remove(seuratF)rerun seurat after filter
seuratA <- NormalizeData (object = seuratA)
seuratA <- FindVariableFeatures(object = seuratA)
seuratA <- ScaleData(object = seuratA, verbose = TRUE)
seuratA <- RunPCA(object=seuratA, npcs = 30, verbose = FALSE)
seuratA <- RunTSNE(object=seuratA, reduction="pca", dims = 1:20)
seuratA <- RunUMAP(object=seuratA, reduction="pca", dims = 1:20)
seuratA <- FindNeighbors(object = seuratA, reduction = "pca", dims= 1:20)
res <- c(0.25, 0.6, 0.8, 0.4)
for (i in 1:length(res)) {
seuratA <- FindClusters(object = seuratA, resolution = res[i], random.seed = 1234)
}## save object
saveRDS(seuratA, file=paste0(basedir,"/data/LNmLToRev_adultonly_seurat.rds"))load object adult
## load object adult only
fileNam <- paste0(basedir, "/data/LNmLToRev_adultonly_seurat.rds")
seuratA <- readRDS(fileNam)
table(seuratA$dataset)
380131_11-11_20250305_Mu_Cxcl13EYFP_Adult_pLN_FRC 380131_12-12_20250305_Mu_Cxcl13EYFP_Adult_mLN_FRC
5253 6419
382581_08-8_20250320_Mu_Cxcl13EYFP_Adult_pLN_FRC 382581_09-9_20250320_Mu_Cxcl13EYFP_Adult_mLN_FRC
7094 3522 set color vectors
colLoc <- c("#61baba", "#ba6161")
names(colLoc) <- unique(seuratA$location)
colLab <- c("#42a071", "#900C3F","#b66e8d", "#8F7700FF", "#61a4ba","#003C67FF",
"#e3953d","#ab5711", "#714542", "#b6856e", "#FFC300")
names(colLab) <- c("FDC", "TRC", "TBRC", "IFRC", "medRC1" , "medRC2",
"PRC1", "PRC2", "Pi16+RC", "PRC3", "VSMC")
coltimepoint <- c("#440154FF", "#3B528BFF", "#21908CFF", "#5DC863FF")
names(coltimepoint) <- c("E18", "P7", "3w", "8w")
collocation <- c("#61baba", "#ba6161")
names(collocation) <- c("iLN", "mLN")plot umaps
clustering
colPal <- c("#DAF7A6", "#FFC300", "#FF5733", "#C70039", "#900C3F", "#b66e8d",
"#61a4ba", "#6178ba", "#54a87f", "#25328a", "#b6856e",
"#ba6161", "#20714a", "#0073C2FF", "#EFC000FF", "#868686FF",
"#CD534CFF","#7AA6DCFF", "#003C67FF", "#8F7700FF", "#3B3B3BFF",
"#A73030FF", "#4A6990FF")[1:length(unique(seuratA$RNA_snn_res.0.25))]
names(colPal) <- unique(seuratA$RNA_snn_res.0.25)
DimPlot(seuratA, reduction = "umap", group.by = "RNA_snn_res.0.25", cols = colPal,
pt.size = 0.1, label = T, shuffle = T) +
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("umap1") +
ylab("umap2")
location
DimPlot(seuratA, reduction = "umap", group.by = "location", cols = collocation,
pt.size = 0.1, label = T, shuffle = T) +
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("umap1") +
ylab("umap2")
plot features
selGenesViolin <- c("ENSMUSG00000022037.Clu", "ENSMUSG00000094686.Ccl21a",
"ENSMUSG00000074934.Grem1", "ENSMUSG00000050069.Grem2",
"ENSMUSG00000042436.Mfap4", "ENSMUSG00000071005.Ccl19",
"ENSMUSG00000016494.Cd34", "ENSMUSG00000001119.Col6a1",
"ENSMUSG00000020241.Col6a2","Rosa26eyfp.Rosa26eyfp",
"ENSMUSG00000023078.Cxcl13", "ENSMUSG00000032135.Mcam")
pList <- sapply(selGenesViolin, function(x){
p <- FeaturePlot(seuratA, reduction = "umap",
features = x,
cols=c("lightgrey", "darkred"),
order = T)+
theme(legend.position="right")
plot(p)
})
integrate data across location
Idents(seuratA) <- seuratA$location
seurat.list <- SplitObject(object = seuratA, split.by = "location")
for (i in 1:length(x = seurat.list)) {
seurat.list[[i]] <- NormalizeData(object = seurat.list[[i]],
verbose = FALSE)
seurat.list[[i]] <- FindVariableFeatures(object = seurat.list[[i]],
selection.method = "vst", nfeatures = 2000, verbose = FALSE)
}
seurat.anchors <- FindIntegrationAnchors(object.list = seurat.list, dims = 1:20)
seuratA.int <- IntegrateData(anchorset = seurat.anchors, dims = 1:20)
DefaultAssay(object = seuratA.int) <- "integrated"
## rerun seurat
seuratA.int <- ScaleData(object = seuratA.int, verbose = FALSE,
features = rownames(seuratA.int))
seuratA.int <- RunPCA(object = seuratA.int, npcs = 20, verbose = FALSE)
seuratA.int <- RunTSNE(object = seuratA.int, recuction = "pca", dims = 1:20)
seuratA.int <- RunUMAP(object = seuratA.int, recuction = "pca", dims = 1:20)
seuratA.int <- FindNeighbors(object = seuratA.int, reduction = "pca", dims = 1:20)
res <- c(0.6, 0.8, 0.4, 0.25)
for (i in 1:length(res)){
seuratA.int <- FindClusters(object = seuratA.int, resolution = res[i],
random.seed = 1234)
}saveRDS(seuratA.int, file=paste0(basedir,"/data/LNmLToRev_adultonly_seurat.integrated.rds"))load integrated object adult
fileNam <- paste0(basedir, "/data/LNmLToRev_adultonly_seurat.integrated.rds")
seuratA.int <- readRDS(fileNam)DefaultAssay(object = seuratA.int) <- "RNA"
seuratA.int$intCluster <- seuratA.int$integrated_snn_res.0.4
Idents(seuratA.int) <- seuratA.int$intCluster
colPal <- c("#DAF7A6", "#FFC300", "#FF5733", "#C70039", "#900C3F", "#b66e8d",
"#61a4ba", "#6178ba", "#54a87f", "#25328a", "#b6856e",
"#ba6161", "#20714a", "#0073C2FF", "#EFC000FF", "#868686FF",
"#CD534CFF","#7AA6DCFF", "#003C67FF", "#8F7700FF", "#3B3B3BFF",
"#A73030FF", "#4A6990FF")[1:length(unique(seuratA.int$intCluster))]
names(colPal) <- unique(seuratA.int$intCluster)assign label
seuratA.int$label <- "label"
seuratA.int$label[which(seuratA.int$intCluster == "0")] <- "MedRC/IFRC"
seuratA.int$label[which(seuratA.int$intCluster == "1")] <- "actMedRC"
seuratA.int$label[which(seuratA.int$intCluster == "2")] <- "TBRC"
seuratA.int$label[which(seuratA.int$intCluster == "3")] <- "TRC"
seuratA.int$label[which(seuratA.int$intCluster == "4")] <- "MedRC"
seuratA.int$label[which(seuratA.int$intCluster == "5")] <- "PRC"
seuratA.int$label[which(seuratA.int$intCluster == "6")] <- "FDC/MRC"
seuratA.int$label[which(seuratA.int$intCluster == "7")] <- "Pi16+RC"
seuratA.int$label[which(seuratA.int$intCluster == "8")] <- "VSMC"
table(seuratA.int$label)
actMedRC FDC/MRC MedRC MedRC/IFRC Pi16+RC PRC TBRC TRC VSMC
4020 1292 2371 4868 600 2192 3841 2839 265 colLab2 <- colLab
colLab <- c("#42a071", "#900C3F","#b66e8d", "#61a4ba", "#424671", "#003C67FF",
"#e3953d", "#714542", "#b6856e", "#FFC300")
names(colLab) <- c("FDC/MRC", "TRC", "TBRC", "MedRC/IFRC", "MedRC" , "actMedRC",
"PRC", "Pi16+RC", "VSMC")Dimplot int data
clustering
DimPlot(seuratA.int, reduction = "umap",
pt.size = 0.1, label = T, shuffle = T, cols = colPal) +
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("umap1") +
ylab("umap2")
label
DimPlot(seuratA.int, 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")
DimPlot(seuratA.int, reduction = "umap", group.by = "label", pt.size=0.5,
cols = colLab, shuffle = T)+
theme_void()
DimPlot(seuratA.int, reduction = "umap", group.by = "label", pt.size=0.5,
cols = colLab, shuffle = T)+
theme_void() +
theme(legend.position = "none") 
label mLN iLN sep
mLN
seuratA.intmLN <- subset(seuratA.int, location == "mLN")
DimPlot(seuratA.intmLN, reduction = "umap", group.by = "label", pt.size=0.5,
cols = colLab, shuffle = T)+
theme_void()
DimPlot(seuratA.intmLN, reduction = "umap", group.by = "label", pt.size=0.5,
cols = colLab, shuffle = T)+
theme_void() +
theme(legend.position = "none") 
iLN
seuratA.intiLN <- subset(seuratA.int, location == "iLN")
DimPlot(seuratA.intiLN, reduction = "umap", group.by = "label", pt.size=0.5,
cols = colLab, shuffle = T)+
theme_void()
DimPlot(seuratA.intiLN, reduction = "umap", group.by = "label", pt.size=0.5,
cols = colLab, shuffle = T)+
theme_void() +
theme(legend.position = "none") 
label split by location
DimPlot(seuratA.int, reduction = "umap", group.by = "label", cols = colLab,
split.by = "location")+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seuratA.int, reduction = "umap", group.by = "label", pt.size=0.5,
cols = colLab, split.by = "location", shuffle = T)+
theme_void()
location
DimPlot(seuratA.int, reduction = "umap", group.by = "location", cols = colLoc,
pt.size = 0.1, shuffle = T) +
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("umap1") +
ylab("umap2")
DimPlot(seuratA.int, reduction = "umap", group.by = "location", pt.size=0.5,
cols = colLoc, split.by = "location", shuffle = T)+
theme_void()
plot features int
selGenesViolin <- c("ENSMUSG00000022037.Clu", "ENSMUSG00000094686.Ccl21a",
"ENSMUSG00000074934.Grem1", "ENSMUSG00000050069.Grem2",
"ENSMUSG00000042436.Mfap4", "ENSMUSG00000071005.Ccl19",
"ENSMUSG00000016494.Cd34", "ENSMUSG00000024011.Pi16",
"ENSMUSG00000001119.Col6a1", "ENSMUSG00000020241.Col6a2",
"Rosa26eyfp.Rosa26eyfp", "ENSMUSG00000023078.Cxcl13",
"ENSMUSG00000032135.Mcam", "ENSMUSG00000023034.Nr4a1")
pList <- sapply(selGenesViolin, function(x){
p <- FeaturePlot(seuratA.int, reduction = "umap",
features = x,
cols=c("lightgrey", "darkred"),
order = F)+
theme(legend.position="right")
plot(p)
})
cluster characterization
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,
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)
}heatmap
seuratA.int <- JoinLayers(seuratA.int)
Idents(seuratA.int) <- seuratA.int$intCluster
seuratAint_markers <- FindAllMarkers(seuratA.int, only.pos = T, logfc.threshold = 0.25)
### plot DE genes top 10 avg logFC
markerAll <- seuratAint_markers %>% group_by(cluster) %>%
mutate(geneID = gene) %>% top_n(10, avg_log2FC) %>%
mutate(gene=gsub(".*\\.", "", geneID)) %>%
filter(nchar(gene)>1)
grpCnt <- markerAll %>% group_by(cluster) %>% summarise(cnt=n())
gapR <- data.frame(cluster=unique(markerAll$cluster)) %>%
left_join(.,grpCnt, by="cluster") %>% mutate(cumSum=cumsum(cnt))
ordVec <- levels(seuratA.int)
pOut <- avgHeatmap(seurat = seuratA.int, selGenes = markerAll,
colVecIdent = colPal,
ordVec=ordVec,
gapVecR=gapR$cumSum, gapVecC=NULL,cc=T,
cr=F, condCol=F)
session info
date()[1] "Fri Jul 11 16:38:30 2025"sessionInfo()R version 4.4.0 (2024-04-24)
Platform: x86_64-apple-darwin20
Running under: macOS Ventura 13.7.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Zurich
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] future_1.58.0 NCmisc_1.2.0 ggupset_0.4.1
[4] gridExtra_2.3 DOSE_3.30.5 enrichplot_1.24.4
[7] msigdbr_24.1.0 org.Hs.eg.db_3.19.1 AnnotationDbi_1.66.0
[10] clusterProfiler_4.12.6 multtest_2.60.0 metap_1.12
[13] scater_1.32.1 scuttle_1.14.0 destiny_3.18.0
[16] circlize_0.4.16 muscat_1.18.0 here_1.0.1
[19] viridis_0.6.5 viridisLite_0.4.2 lubridate_1.9.4
[22] forcats_1.0.0 stringr_1.5.1 purrr_1.0.4
[25] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[28] tidyverse_2.0.0 dplyr_1.1.4 SingleCellExperiment_1.26.0
[31] SummarizedExperiment_1.34.0 Biobase_2.64.0 GenomicRanges_1.56.2
[34] GenomeInfoDb_1.40.1 IRanges_2.38.1 S4Vectors_0.42.1
[37] BiocGenerics_0.50.0 MatrixGenerics_1.16.0 matrixStats_1.5.0
[40] pheatmap_1.0.13 ggpubr_0.6.0 ggplot2_3.5.2
[43] Seurat_5.3.0 SeuratObject_5.1.0 sp_2.2-0
[46] runSeurat3_0.1.0 ExploreSCdataSeurat3_0.1.0
loaded via a namespace (and not attached):
[1] igraph_2.1.4 ica_1.0-3 plotly_4.10.4
[4] Formula_1.2-5 zlibbioc_1.50.0 tidyselect_1.2.1
[7] bit_4.6.0 doParallel_1.0.17 clue_0.3-66
[10] lattice_0.22-7 rjson_0.2.23 blob_1.2.4
[13] S4Arrays_1.4.1 pbkrtest_0.5.4 parallel_4.4.0
[16] png_0.1-8 plotrix_3.8-4 cli_3.6.5
[19] ggplotify_0.1.2 goftest_1.2-3 VIM_6.2.2
[22] variancePartition_1.34.0 BiocNeighbors_1.22.0 shadowtext_0.1.4
[25] uwot_0.2.3 curl_6.2.3 tidytree_0.4.6
[28] mime_0.13 evaluate_1.0.3 ComplexHeatmap_2.20.0
[31] stringi_1.8.7 backports_1.5.0 lmerTest_3.1-3
[34] qqconf_1.3.2 httpuv_1.6.16 magrittr_2.0.3
[37] rappdirs_0.3.3 splines_4.4.0 ggraph_2.2.1
[40] sctransform_0.4.2 ggbeeswarm_0.7.2 DBI_1.2.3
[43] jquerylib_0.1.4 smoother_1.3 withr_3.0.2
[46] git2r_0.36.2 corpcor_1.6.10 reformulas_0.4.1
[49] class_7.3-23 rprojroot_2.0.4 lmtest_0.9-40
[52] tidygraph_1.3.1 colourpicker_1.3.0 htmlwidgets_1.6.4
[55] fs_1.6.6 ggrepel_0.9.6 labeling_0.4.3
[58] fANCOVA_0.6-1 SparseArray_1.4.8 DESeq2_1.44.0
[61] ranger_0.17.0 DEoptimR_1.1-3-1 reticulate_1.42.0
[64] hexbin_1.28.5 zoo_1.8-14 XVector_0.44.0
[67] knitr_1.50 ggplot.multistats_1.0.1 UCSC.utils_1.0.0
[70] RhpcBLASctl_0.23-42 timechange_0.3.0 foreach_1.5.2
[73] patchwork_1.3.0 caTools_1.18.3 ggtree_3.12.0
[76] data.table_1.17.4 R.oo_1.27.1 RSpectra_0.16-2
[79] irlba_2.3.5.1 fastDummies_1.7.5 gridGraphics_0.5-1
[82] lazyeval_0.2.2 yaml_2.3.10 survival_3.8-3
[85] scattermore_1.2 crayon_1.5.3 RcppAnnoy_0.0.22
[88] RColorBrewer_1.1-3 progressr_0.15.1 tweenr_2.0.3
[91] later_1.4.2 ggridges_0.5.6 codetools_0.2-20
[94] GlobalOptions_0.1.2 aod_1.3.3 KEGGREST_1.44.1
[97] Rtsne_0.17 shape_1.4.6.1 limma_3.60.6
[100] pkgconfig_2.0.3 TMB_1.9.17 spatstat.univar_3.1-3
[103] mathjaxr_1.8-0 EnvStats_3.1.0 aplot_0.2.5
[106] scatterplot3d_0.3-44 ape_5.8-1 spatstat.sparse_3.1-0
[109] xtable_1.8-4 car_3.1-3 plyr_1.8.9
[112] httr_1.4.7 rbibutils_2.3 tools_4.4.0
[115] globals_0.18.0 beeswarm_0.4.0 broom_1.0.8
[118] nlme_3.1-168 assertthat_0.2.1 lme4_1.1-37
[121] digest_0.6.37 numDeriv_2016.8-1.1 Matrix_1.7-3
[124] farver_2.1.2 tzdb_0.5.0 remaCor_0.0.18
[127] reshape2_1.4.4 yulab.utils_0.2.0 glue_1.8.0
[130] cachem_1.1.0 polyclip_1.10-7 generics_0.1.4
[133] Biostrings_2.72.1 mvtnorm_1.3-3 presto_1.0.0
[136] parallelly_1.45.0 mnormt_2.1.1 statmod_1.5.0
[139] RcppHNSW_0.6.0 ScaledMatrix_1.12.0 carData_3.0-5
[142] minqa_1.2.8 pbapply_1.7-2 httr2_1.1.2
[145] spam_2.11-1 gson_0.1.0 graphlayouts_1.2.2
[148] gtools_3.9.5 ggsignif_0.6.4 RcppEigen_0.3.4.0.2
[151] shiny_1.10.0 GenomeInfoDbData_1.2.12 glmmTMB_1.1.11
[154] R.utils_2.13.0 memoise_2.0.1 rmarkdown_2.29
[157] scales_1.4.0 R.methodsS3_1.8.2 RANN_2.6.2
[160] Cairo_1.6-2 spatstat.data_3.1-6 rstudioapi_0.17.1
[163] cluster_2.1.8.1 mutoss_0.1-13 spatstat.utils_3.1-4
[166] hms_1.1.3 fitdistrplus_1.2-2 cowplot_1.1.3
[169] colorspace_2.1-1 rlang_1.1.6 DelayedMatrixStats_1.26.0
[172] sparseMatrixStats_1.16.0 xts_0.14.1 dotCall64_1.2
[175] shinydashboard_0.7.3 ggforce_0.4.2 laeken_0.5.3
[178] mgcv_1.9-3 xfun_0.52 e1071_1.7-16
[181] TH.data_1.1-3 iterators_1.0.14 abind_1.4-8
[184] GOSemSim_2.30.2 treeio_1.28.0 bitops_1.0-9
[187] Rdpack_2.6.4 promises_1.3.3 scatterpie_0.2.4
[190] RSQLite_2.4.0 qvalue_2.36.0 sandwich_3.1-1
[193] fgsea_1.30.0 DelayedArray_0.30.1 proxy_0.4-27
[196] GO.db_3.19.1 compiler_4.4.0 prettyunits_1.2.0
[199] boot_1.3-31 beachmat_2.20.0 listenv_0.9.1
[202] Rcpp_1.0.14 edgeR_4.2.2 workflowr_1.7.1
[205] BiocSingular_1.20.0 tensor_1.5 MASS_7.3-65
[208] progress_1.2.3 BiocParallel_1.38.0 babelgene_22.9
[211] spatstat.random_3.4-1 R6_2.6.1 fastmap_1.2.0
[214] multcomp_1.4-28 fastmatch_1.1-6 rstatix_0.7.2
[217] vipor_0.4.7 TTR_0.24.4 ROCR_1.0-11
[220] TFisher_0.2.0 rsvd_1.0.5 vcd_1.4-13
[223] nnet_7.3-20 gtable_0.3.6 KernSmooth_2.23-26
[226] miniUI_0.1.2 deldir_2.0-4 htmltools_0.5.8.1
[229] ggthemes_5.1.0 bit64_4.6.0-1 spatstat.explore_3.4-3
[232] lifecycle_1.0.4 blme_1.0-6 nloptr_2.2.1
[235] sass_0.4.10 vctrs_0.6.5 robustbase_0.99-4-1
[238] spatstat.geom_3.4-1 sn_2.1.1 ggfun_0.1.8
[241] future.apply_1.11.3 bslib_0.9.0 pillar_1.10.2
[244] gplots_3.2.0 pcaMethods_1.96.0 locfit_1.5-9.12
[247] jsonlite_2.0.0 GetoptLong_1.0.5
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: x86_64-apple-darwin20
Running under: macOS Ventura 13.7.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Zurich
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] future_1.58.0 NCmisc_1.2.0 ggupset_0.4.1
[4] gridExtra_2.3 DOSE_3.30.5 enrichplot_1.24.4
[7] msigdbr_24.1.0 org.Hs.eg.db_3.19.1 AnnotationDbi_1.66.0
[10] clusterProfiler_4.12.6 multtest_2.60.0 metap_1.12
[13] scater_1.32.1 scuttle_1.14.0 destiny_3.18.0
[16] circlize_0.4.16 muscat_1.18.0 here_1.0.1
[19] viridis_0.6.5 viridisLite_0.4.2 lubridate_1.9.4
[22] forcats_1.0.0 stringr_1.5.1 purrr_1.0.4
[25] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[28] tidyverse_2.0.0 dplyr_1.1.4 SingleCellExperiment_1.26.0
[31] SummarizedExperiment_1.34.0 Biobase_2.64.0 GenomicRanges_1.56.2
[34] GenomeInfoDb_1.40.1 IRanges_2.38.1 S4Vectors_0.42.1
[37] BiocGenerics_0.50.0 MatrixGenerics_1.16.0 matrixStats_1.5.0
[40] pheatmap_1.0.13 ggpubr_0.6.0 ggplot2_3.5.2
[43] Seurat_5.3.0 SeuratObject_5.1.0 sp_2.2-0
[46] runSeurat3_0.1.0 ExploreSCdataSeurat3_0.1.0
loaded via a namespace (and not attached):
[1] igraph_2.1.4 ica_1.0-3 plotly_4.10.4
[4] Formula_1.2-5 zlibbioc_1.50.0 tidyselect_1.2.1
[7] bit_4.6.0 doParallel_1.0.17 clue_0.3-66
[10] lattice_0.22-7 rjson_0.2.23 blob_1.2.4
[13] S4Arrays_1.4.1 pbkrtest_0.5.4 parallel_4.4.0
[16] png_0.1-8 plotrix_3.8-4 cli_3.6.5
[19] ggplotify_0.1.2 goftest_1.2-3 VIM_6.2.2
[22] variancePartition_1.34.0 BiocNeighbors_1.22.0 shadowtext_0.1.4
[25] uwot_0.2.3 curl_6.2.3 tidytree_0.4.6
[28] mime_0.13 evaluate_1.0.3 ComplexHeatmap_2.20.0
[31] stringi_1.8.7 backports_1.5.0 lmerTest_3.1-3
[34] qqconf_1.3.2 httpuv_1.6.16 magrittr_2.0.3
[37] rappdirs_0.3.3 splines_4.4.0 ggraph_2.2.1
[40] sctransform_0.4.2 ggbeeswarm_0.7.2 DBI_1.2.3
[43] jquerylib_0.1.4 smoother_1.3 withr_3.0.2
[46] git2r_0.36.2 corpcor_1.6.10 reformulas_0.4.1
[49] class_7.3-23 rprojroot_2.0.4 lmtest_0.9-40
[52] tidygraph_1.3.1 colourpicker_1.3.0 htmlwidgets_1.6.4
[55] fs_1.6.6 ggrepel_0.9.6 labeling_0.4.3
[58] fANCOVA_0.6-1 SparseArray_1.4.8 DESeq2_1.44.0
[61] ranger_0.17.0 DEoptimR_1.1-3-1 reticulate_1.42.0
[64] hexbin_1.28.5 zoo_1.8-14 XVector_0.44.0
[67] knitr_1.50 ggplot.multistats_1.0.1 UCSC.utils_1.0.0
[70] RhpcBLASctl_0.23-42 timechange_0.3.0 foreach_1.5.2
[73] patchwork_1.3.0 caTools_1.18.3 ggtree_3.12.0
[76] data.table_1.17.4 R.oo_1.27.1 RSpectra_0.16-2
[79] irlba_2.3.5.1 fastDummies_1.7.5 gridGraphics_0.5-1
[82] lazyeval_0.2.2 yaml_2.3.10 survival_3.8-3
[85] scattermore_1.2 crayon_1.5.3 RcppAnnoy_0.0.22
[88] RColorBrewer_1.1-3 progressr_0.15.1 tweenr_2.0.3
[91] later_1.4.2 ggridges_0.5.6 codetools_0.2-20
[94] GlobalOptions_0.1.2 aod_1.3.3 KEGGREST_1.44.1
[97] Rtsne_0.17 shape_1.4.6.1 limma_3.60.6
[100] pkgconfig_2.0.3 TMB_1.9.17 spatstat.univar_3.1-3
[103] mathjaxr_1.8-0 EnvStats_3.1.0 aplot_0.2.5
[106] scatterplot3d_0.3-44 ape_5.8-1 spatstat.sparse_3.1-0
[109] xtable_1.8-4 car_3.1-3 plyr_1.8.9
[112] httr_1.4.7 rbibutils_2.3 tools_4.4.0
[115] globals_0.18.0 beeswarm_0.4.0 broom_1.0.8
[118] nlme_3.1-168 assertthat_0.2.1 lme4_1.1-37
[121] digest_0.6.37 numDeriv_2016.8-1.1 Matrix_1.7-3
[124] farver_2.1.2 tzdb_0.5.0 remaCor_0.0.18
[127] reshape2_1.4.4 yulab.utils_0.2.0 glue_1.8.0
[130] cachem_1.1.0 polyclip_1.10-7 generics_0.1.4
[133] Biostrings_2.72.1 mvtnorm_1.3-3 presto_1.0.0
[136] parallelly_1.45.0 mnormt_2.1.1 statmod_1.5.0
[139] RcppHNSW_0.6.0 ScaledMatrix_1.12.0 carData_3.0-5
[142] minqa_1.2.8 pbapply_1.7-2 httr2_1.1.2
[145] spam_2.11-1 gson_0.1.0 graphlayouts_1.2.2
[148] gtools_3.9.5 ggsignif_0.6.4 RcppEigen_0.3.4.0.2
[151] shiny_1.10.0 GenomeInfoDbData_1.2.12 glmmTMB_1.1.11
[154] R.utils_2.13.0 memoise_2.0.1 rmarkdown_2.29
[157] scales_1.4.0 R.methodsS3_1.8.2 RANN_2.6.2
[160] Cairo_1.6-2 spatstat.data_3.1-6 rstudioapi_0.17.1
[163] cluster_2.1.8.1 mutoss_0.1-13 spatstat.utils_3.1-4
[166] hms_1.1.3 fitdistrplus_1.2-2 cowplot_1.1.3
[169] colorspace_2.1-1 rlang_1.1.6 DelayedMatrixStats_1.26.0
[172] sparseMatrixStats_1.16.0 xts_0.14.1 dotCall64_1.2
[175] shinydashboard_0.7.3 ggforce_0.4.2 laeken_0.5.3
[178] mgcv_1.9-3 xfun_0.52 e1071_1.7-16
[181] TH.data_1.1-3 iterators_1.0.14 abind_1.4-8
[184] GOSemSim_2.30.2 treeio_1.28.0 bitops_1.0-9
[187] Rdpack_2.6.4 promises_1.3.3 scatterpie_0.2.4
[190] RSQLite_2.4.0 qvalue_2.36.0 sandwich_3.1-1
[193] fgsea_1.30.0 DelayedArray_0.30.1 proxy_0.4-27
[196] GO.db_3.19.1 compiler_4.4.0 prettyunits_1.2.0
[199] boot_1.3-31 beachmat_2.20.0 listenv_0.9.1
[202] Rcpp_1.0.14 edgeR_4.2.2 workflowr_1.7.1
[205] BiocSingular_1.20.0 tensor_1.5 MASS_7.3-65
[208] progress_1.2.3 BiocParallel_1.38.0 babelgene_22.9
[211] spatstat.random_3.4-1 R6_2.6.1 fastmap_1.2.0
[214] multcomp_1.4-28 fastmatch_1.1-6 rstatix_0.7.2
[217] vipor_0.4.7 TTR_0.24.4 ROCR_1.0-11
[220] TFisher_0.2.0 rsvd_1.0.5 vcd_1.4-13
[223] nnet_7.3-20 gtable_0.3.6 KernSmooth_2.23-26
[226] miniUI_0.1.2 deldir_2.0-4 htmltools_0.5.8.1
[229] ggthemes_5.1.0 bit64_4.6.0-1 spatstat.explore_3.4-3
[232] lifecycle_1.0.4 blme_1.0-6 nloptr_2.2.1
[235] sass_0.4.10 vctrs_0.6.5 robustbase_0.99-4-1
[238] spatstat.geom_3.4-1 sn_2.1.1 ggfun_0.1.8
[241] future.apply_1.11.3 bslib_0.9.0 pillar_1.10.2
[244] gplots_3.2.0 pcaMethods_1.96.0 locfit_1.5-9.12
[247] jsonlite_2.0.0 GetoptLong_1.0.5