Last updated: 2025-07-15

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preprocess

load packages

library(ExploreSCdataSeurat3)
library(runSeurat3)
library(Seurat)
library(ggpubr)
library(pheatmap)
library(SingleCellExperiment)
library(dplyr)
library(tidyverse)
library(viridis)
library(muscat)
library(circlize)
library(destiny)
library(scater)
library(metap)
library(multtest)
library(clusterProfiler)
library(org.Mm.eg.db)
library(msigdbr)
library(enrichplot)
library(DOSE)
library(grid)
library(gridExtra)
library(ggupset)
library(VennDiagram)
library(NCmisc)
library(slingshot)
library(here)
library(RColorBrewer)

set color vectors

coltimepoint <- c("#440154FF", "#3B528BFF", "#21908CFF", "#5DC863FF")
names(coltimepoint) <- c("E18", "P7", "3w", "8w")

collocation <- c("#61baba", "#ba6161")
names(collocation) <- c("iLN", "mLN")

load object all

basedir <- here()
fileNam <- paste0(basedir, "/data/AllSamplesMerged_seurat.rds")
seuratM <- readRDS(fileNam)
table(seuratM$age)

      3w       8w E17to7wk      E18       P7 
   12291    32503    19685    33903    10005

subset E18

seuratA <- subset(seuratM, age == "E18")
table(seuratA$age)

  E18 
33903 
## 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: 33903
Number of edges: 1106453

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9482
Number of communities: 16
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 33903
Number of edges: 1106453

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9122
Number of communities: 20
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 33903
Number of edges: 1106453

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8973
Number of communities: 25
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 33903
Number of edges: 1106453

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9310
Number of communities: 18
Elapsed time: 6 seconds

dimplot all

clustering

Idents(seuratA) <- seuratA$RNA_snn_res.0.25
DimPlot(seuratA, reduction = "umap", group.by = "RNA_snn_res.0.25" ,
        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" ,
        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")

features

genes <- data.frame(gene=rownames(seuratA)) %>% 
    mutate(geneID=gsub("^.*\\.", "", gene)) 

selGenes <- data.frame(geneID=c("Ptprc", "Msln", "Mki67", "Kcnn3", "Tcf21", "Pecam1", "Lyve1", "Ccl21a", "Icam1", "Cd34", "Mrc1", "Rosa26eyfp", "Krt19", "Acta2","Myh11", "Cxcl13", "Epcam", "Adipoq")) %>% 
  left_join(., genes, by = "geneID") 

pList <- sapply(selGenes$gene, function(x){
  p <- FeaturePlot(seuratA, reduction = "umap", 
            features = x, 
            cols=c("lightgrey","darkred"),
            order = T)+
  theme(legend.position="right")
  plot(p)
})

filter

## filter and Pecam+ cells (cluster 15)
## filter immune cells (cluseter 14)
table(seuratA$RNA_snn_res.0.25)
seuratF <- subset(seuratA, RNA_snn_res.0.25 %in% c("15", "14"), invert = TRUE)
table(seuratF$RNA_snn_res.0.25)

seuratE18fil <- seuratF
remove(seuratF)

rerun after fil

load object fil

fileNam <- paste0(basedir, "/data/E18fil_seurat.rds")
seuratE18fil <- readRDS(fileNam)

dimplot E18 fil

clustering

Idents(seuratE18fil) <- seuratE18fil$RNA_snn_res.0.25
colPal <- c("#DAF7A6", "#FFC300", "#FF5733", "#C70039", "#900C3F", "#b66e8d",
            "#61a4ba", "#6178ba", "#54a87f",  "#25328a",
            "#b6856e", "#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF", 
            "#7AA6DCFF", "#003C67FF", "#8F7700FF", "#3B3B3BFF", "#A73030FF",
            "#4A6990FF")[1:length(unique(seuratE18fil$RNA_snn_res.0.25))]
names(colPal) <- unique(seuratE18fil$RNA_snn_res.0.25)

DimPlot(seuratE18fil, reduction = "umap", group.by = "RNA_snn_res.0.25",
        cols = colPal, label = TRUE)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

location

DimPlot(seuratE18fil, reduction = "umap", group.by = "location",
        cols = collocation)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

saveRDS(seuratE18fil, file=paste0(basedir, "/data/E18fil_seurat.rds"))

subset EYFP expressing cells

table(seuratE18fil$EYFP)
seuratE18EYFPv2 <- subset(seuratE18fil, EYFP == "pos")
table(seuratE18EYFPv2$EYFP)

DimPlot(seuratE18EYFPv2, reduction = "umap", group.by = "RNA_snn_res.0.25",
        cols = colPal, label = TRUE)

#rerun seurat
seuratE18EYFPv2 <- NormalizeData (object = seuratE18EYFPv2)
seuratE18EYFPv2<- FindVariableFeatures(object = seuratE18EYFPv2)
seuratE18EYFPv2 <- ScaleData(object = seuratE18EYFPv2, verbose = TRUE)
seuratE18EYFPv2 <- RunPCA(object=seuratE18EYFPv2, npcs = 30, verbose = FALSE)
seuratE18EYFPv2 <- RunTSNE(object=seuratE18EYFPv2, reduction="pca", dims = 1:20)
seuratE18EYFPv2 <- RunUMAP(object=seuratE18EYFPv2, reduction="pca", dims = 1:20)
seuratE18EYFPv2 <- FindNeighbors(object = seuratE18EYFPv2, reduction = "pca", dims= 1:20)

res <- c(0.25, 0.6, 0.8, 0.4)
for (i in 1:length(res)) {
  seuratE18EYFPv2 <- FindClusters(object = seuratE18EYFPv2, resolution = res[i], random.seed = 1234)
}
saveRDS(seuratE18EYFPv2, file=paste0(basedir, "/data/E18_EYFPv2_Ccl19_seurat.rds"))

load object E18 EYFP+

fileNam <- paste0(basedir, "/data/E18_EYFPv2_Ccl19_seurat.rds")
seuratE18EYFPv2 <- readRDS(fileNam)

dimplot E18 EYFP+

clustering

Idents(seuratE18EYFPv2) <- seuratE18EYFPv2$RNA_snn_res.0.25
colPal <- c("#DAF7A6", "#FFC300", "#FF5733", "#C70039", "#900C3F", "#b66e8d",
            "#61a4ba", "#6178ba", "#54a87f",  "#25328a",
            "#b6856e", "#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF", 
            "#7AA6DCFF", "#003C67FF", "#8F7700FF", "#3B3B3BFF", "#A73030FF",
            "#4A6990FF")[1:length(unique(seuratE18EYFPv2$RNA_snn_res.0.25))]
names(colPal) <- unique(seuratE18EYFPv2$RNA_snn_res.0.25)

DimPlot(seuratE18EYFPv2, reduction = "umap", group.by = "RNA_snn_res.0.25",
        cols = colPal, label = TRUE)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

location

DimPlot(seuratE18EYFPv2, reduction = "umap", group.by = "location",
        cols = collocation)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

features E18 EYFP+

genes <- data.frame(gene=rownames(seuratE18EYFPv2)) %>% 
    mutate(geneID=gsub("^.*\\.", "", gene)) 

selGenes <- data.frame(geneID=c("Rosa26eyfp", "Mki67", "Acta2", "Myh11", "Ccl19", "Cxcl13", "Cd34", "Icam1","Vcam1", "Pi16")) %>% 
  left_join(., genes, by = "geneID") 

pList <- sapply(selGenes$gene, function(x){
  p <- FeaturePlot(seuratE18EYFPv2, reduction = "umap", 
            features = x, 
            cols=c("lightgrey","darkred"),
            order = T)+
  theme(legend.position="right")
  plot(p)
})

integrate data across location

Idents(seuratE18EYFPv2) <- seuratE18EYFPv2$location

seurat.list <- SplitObject(object = seuratE18EYFPv2, 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)
seuratE18EYFPv2.int <- IntegrateData(anchorset = seurat.anchors, dims = 1:20)
DefaultAssay(object = seuratE18EYFPv2.int) <- "integrated"

## rerun seurat
seuratE18EYFPv2.int <- ScaleData(object = seuratE18EYFPv2.int, verbose = FALSE,
                         features = rownames(seuratE18EYFPv2.int))
seuratE18EYFPv2.int <- RunPCA(object = seuratE18EYFPv2.int, npcs = 20, verbose = FALSE)
seuratE18EYFPv2.int <- RunTSNE(object = seuratE18EYFPv2.int, recuction = "pca", dims = 1:20)
seuratE18EYFPv2.int <- RunUMAP(object = seuratE18EYFPv2.int, recuction = "pca", dims = 1:20)

seuratE18EYFPv2.int <- FindNeighbors(object = seuratE18EYFPv2.int, reduction = "pca", dims = 1:20)
res <- c(0.6, 0.8, 0.4, 0.25)
for (i in 1:length(res)){
  seuratE18EYFPv2.int <- FindClusters(object = seuratE18EYFPv2.int, resolution = res[i],
                              random.seed = 1234)
}
saveRDS(seuratE18EYFPv2.int, file=paste0(basedir, "/data/E18EYFPv2_integrated_Ccl19_seurat.rds"))

load object E18 EYFP+ integrated

fileNam <- paste0(basedir, "/data/E18EYFPv2_integrated_Ccl19_seurat.rds")
seuratE18EYFPv2.int <- readRDS(fileNam)
DefaultAssay(object = seuratE18EYFPv2.int) <- "RNA"
seuratE18EYFPv2.int$intCluster <- seuratE18EYFPv2.int$integrated_snn_res.0.25
Idents(seuratE18EYFPv2.int) <- seuratE18EYFPv2.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(seuratE18EYFPv2.int$intCluster))]
names(colPal) <- unique(seuratE18EYFPv2.int$intCluster)

dimplot E18 EYFP+ int

clustering

DimPlot(seuratE18EYFPv2.int, reduction = "umap",
        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

DimPlot(seuratE18EYFPv2.int, reduction = "umap", group.by = "location", cols = collocation,
        shuffle = T) +
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("umap1") +
  ylab("umap2")

assign label

seuratE18EYFPv2.int$label <- "label"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "0")] <- "cluster1"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "1")] <- "Prolif"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "2")] <- "cluster3"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "3")] <- "cluster4"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "4")] <- "cluster2"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "5")] <- "Adipoc"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "6")] <- "Mesothelial"

table(seuratE18EYFPv2.int$label)

     Adipoc    cluster1    cluster2    cluster3    cluster4 Mesothelial      Prolif 
         17         852         292         514         450          15         591 
##order
seuratE18EYFPv2.int$label <- factor(seuratE18EYFPv2.int$label, levels = c("cluster1", "cluster2", "cluster3", "cluster4", "Adipoc", "Mesothelial", "Prolif"))
table(seuratE18EYFPv2.int$label)

   cluster1    cluster2    cluster3    cluster4      Adipoc Mesothelial      Prolif 
        852         292         514         450          17          15         591 
colLab <- c("#900C3F","#b66e8d", "#003C67FF",
            "#e3953d", "#714542", "#b6856e","grey")
names(colLab) <- c("cluster1", "cluster2", "cluster3", "cluster4", "Adipoc", "Mesothelial", "Prolif")

label

colLab <- c("#900C3F","#b66e8d", "#003C67FF",
            "#e3953d", "#714542", "#b6856e","grey")
names(colLab) <- c("cluster1", "cluster2", "cluster3", "cluster4", "Adipoc", "Mesothelial", "Prolif")

DimPlot(seuratE18EYFPv2.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(seuratE18EYFPv2.int, reduction = "umap", group.by = "label", pt.size=0.5,
        cols = colLab, shuffle = T)+
  theme_void()

DimPlot(seuratE18EYFPv2.int, reduction = "umap", group.by = "label", pt.size=0.5,
        cols = colLab, shuffle = T)+
  theme_void() + 
  theme(legend.position = "none")

dotplot FRC marker E18 EYFP+ int

seurat_markers <- data.frame(gene=c("Vcam1", "Icam1",
                                    "Cxcl13", "Ccl19", "Ccl21a","Tnfsf11", "Grem1","Ifitm1","Cxcl1","Ifitm3","Ccl2","Nfkbia","Des",
                                    "Mfap5","Cdkn1c","Akap12","Anxa2","Lox","Gsn","Basp1","Fndc1","Sparc","Col1a1","Fbn2","Nr4a1","Fbln1","Cd34","Pi16",
                                    "Fbln5","Tm4sf1", "Ntrk3", "Fhl1", "Rgs7bp", "Adamts2", "Mpped2", "Ramp1", "Pdgfrl", "Eln", "Hspb2","Mgp", "Actg2","Acta2", "Myh11","Mcam","Adipoq", "Mki67", "Msln"))

genes <- data.frame(geneID=rownames(seuratE18EYFPv2.int)) %>%
  mutate(gene=gsub(".*\\.", "",  geneID)) 

markerAll <- seurat_markers %>% left_join(., genes, by="gene")


## Dotplot all
Idents(seuratE18EYFPv2.int) <- seuratE18EYFPv2.int$label

DotPlot(seuratE18EYFPv2.int, assay="RNA", features = rev(markerAll$geneID), scale =T,
        cluster.idents = F) +
  scale_color_viridis_c() +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_discrete(breaks=rev(markerAll$geneID), labels=rev(markerAll$gene)) +
  xlab("") + ylab("")

subset FRCs and rerun

table(seuratE18EYFPv2.int$label)

   cluster1    cluster2    cluster3    cluster4      Adipoc Mesothelial      Prolif 
        852         292         514         450          17          15         591 
seuratE18EYFPv2.int <- subset(seuratE18EYFPv2.int, label %in% c("Adipoc", "Mesothelial"), invert = TRUE)
table(seuratE18EYFPv2.int$label)

cluster1 cluster2 cluster3 cluster4   Prolif 
     852      292      514      450      591 
table(seuratE18EYFPv2.int$orig.ident)

     
2699 
## rerun seurat
DefaultAssay(object = seuratE18EYFPv2.int) <- "integrated"
seuratE18EYFPv2.int <- ScaleData(object = seuratE18EYFPv2.int, verbose = FALSE,
                         features = rownames(seuratE18EYFPv2.int))
seuratE18EYFPv2.int <- RunPCA(object = seuratE18EYFPv2.int, npcs = 20, verbose = FALSE)
seuratE18EYFPv2.int <- RunTSNE(object = seuratE18EYFPv2.int, recuction = "pca", dims = 1:20)
seuratE18EYFPv2.int <- RunUMAP(object = seuratE18EYFPv2.int, recuction = "pca", dims = 1:20)

seuratE18EYFPv2.int <- FindNeighbors(object = seuratE18EYFPv2.int, reduction = "pca", dims = 1:20)
res <- c(0.1, 0.6, 0.8, 0.4, 0.25)
for (i in 1:length(res)){
  seuratE18EYFPv2.int <- FindClusters(object = seuratE18EYFPv2.int, resolution = res[i],
                              random.seed = 1234)
}
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2699
Number of edges: 93881

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9410
Number of communities: 3
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2699
Number of edges: 93881

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8333
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2699
Number of edges: 93881

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8082
Number of communities: 12
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2699
Number of edges: 93881

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8679
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2699
Number of edges: 93881

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8977
Number of communities: 5
Elapsed time: 0 seconds
DefaultAssay(object = seuratE18EYFPv2.int) <- "RNA"
seuratE18EYFPv2.int$intCluster <- seuratE18EYFPv2.int$integrated_snn_res.0.1
Idents(seuratE18EYFPv2.int) <- seuratE18EYFPv2.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(seuratE18EYFPv2.int$intCluster))]
names(colPal) <- unique(seuratE18EYFPv2.int$intCluster)

dimplot E18 EYFP+ fil

clustering

DimPlot(seuratE18EYFPv2.int, reduction = "umap",
        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

DimPlot(seuratE18EYFPv2.int, reduction = "umap", group.by = "location", cols = collocation,
        shuffle = T) +
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("umap1") +
  ylab("umap2")

assign label

seuratE18EYFPv2.int$label <- "label"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "0")] <- "cluster2"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "1")] <- "cluster3"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "2")] <- "cluster1"

table(seuratE18EYFPv2.int$label)

cluster1 cluster2 cluster3 
     760     1164      775 
##order
seuratE18EYFPv2.int$label <- factor(seuratE18EYFPv2.int$label, levels = c("cluster2", "cluster3", "cluster1"))
table(seuratE18EYFPv2.int$label)

cluster2 cluster3 cluster1 
    1164      775      760 
colLab <- c("#900C3F","#b66e8d", "#003C67FF")
names(colLab) <- c("cluster2", "cluster3", "cluster1")

label

DimPlot(seuratE18EYFPv2.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(seuratE18EYFPv2.int, reduction = "umap", group.by = "label", pt.size=0.5,
        cols = colLab, shuffle = T)+
  theme_void()

DimPlot(seuratE18EYFPv2.int, reduction = "umap", group.by = "label", pt.size=0.5,
        cols = colLab, shuffle = T)+
  theme_void() + 
  theme(legend.position = "none")

featrue Ccl19

genes <- data.frame(gene=rownames(seuratE18EYFPv2.int)) %>% 
    mutate(geneID=gsub("^.*\\.", "", gene)) 

selGenes <- data.frame(geneID=c("Ccl19")) %>% 
  left_join(., genes, by = "geneID") 

pList <- sapply(selGenes$gene, function(x){
  p <- FeaturePlot(seuratE18EYFPv2.int, reduction = "umap", 
            features = x, 
            cols=c("lightgrey","darkred"),
            order = FALSE)+
  theme(legend.position="right")
  plot(p)
})

dotplot marker E18 EYFP+ fil

seurat_markers <- data.frame(gene=c("Vcam1", "Icam1",
                                    "Cxcl13", "Ccl19", "Ccl21a","Tnfsf11", "Grem1","Ifitm1","Cxcl1","Ifitm3","Ccl2","Nfkbia","Des",
                                    "Mfap5","Cdkn1c","Akap12","Anxa2","Lox","Gsn","Basp1","Fndc1","Sparc","Col1a1","Fbn2","Nr4a1","Fbln1","Cd34","Pi16",
                                    "Fbln5","Tm4sf1", "Ntrk3", "Fhl1", "Rgs7bp", "Adamts2", "Mpped2", "Ramp1", "Pdgfrl", "Eln", "Hspb2","Mgp", "Actg2","Acta2", "Myh11", "Mcam", "Mki67", "Ccna2", "Cdca8", "Prc1", "Aurkb"))

genes <- data.frame(geneID=rownames(seuratE18EYFPv2.int)) %>%
  mutate(gene=gsub(".*\\.", "",  geneID)) 

markerAll <- seurat_markers %>% left_join(., genes, by="gene")


## Dotplot all
Idents(seuratE18EYFPv2.int) <- seuratE18EYFPv2.int$label

DotPlot(seuratE18EYFPv2.int, assay="RNA", features = rev(markerAll$geneID), scale =T,
        cluster.idents = F) +
  scale_color_viridis_c() +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_discrete(breaks=rev(markerAll$geneID), labels=rev(markerAll$gene)) +
  xlab("") + ylab("")

signatures

## convert seurat object to sce object
## exteract logcounts
logcounts <- GetAssayData(seuratE18EYFPv2.int, assay = "RNA", slot = "data")
counts <- GetAssayData(seuratE18EYFPv2.int, assay = "RNA", slot = "counts")
## extract reduced dims from integrated assay
pca <- Embeddings(seuratE18EYFPv2.int, reduction = "pca")
umap <- Embeddings(seuratE18EYFPv2.int, reduction = "umap")
## create sce object
sce <- SingleCellExperiment(assays =list (
  counts = counts,
  logcounts = logcounts
),
colData = seuratE18EYFPv2.int@meta.data,
rowData = data.frame(gene_id = rownames(logcounts)),
reducedDims = SimpleList(
  PCA = pca,
  UMAP = umap
))

genes <- data.frame(geneID=rownames(sce)) %>% mutate(gene=gsub(".*\\.", "", geneID))
pal = colorRampPalette(c("#053061", "#2166ac", "#f7f7f7", "#f4a582", "#b2183c", "#85122d"))
selGenes <- data.frame(gene=c("Cxcl13", "Ccl19", "Ccl21a","Tnfsf11", "Grem1"))
signGenes <- genes %>% dplyr::filter(gene %in% selGenes$gene)

##make a count matrix of signature genes
sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
cntMat <- rowSums(t(as.matrix(
    sceSub@assays@data$logcounts)))/nrow(signGenes)
sceSub$sign <- cntMat
sceSub$sign2 <- sceSub$sign
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, 3))
sceSub$sign2[which(sceSub$sign > 3)] <- 3
##check max and min values
max(sceSub$sign)
[1] 2.890401
plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc +
  theme(legend.position = "none")

plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc

selGenes <- data.frame(gene=c("Mfap5","Gsn","Fndc1","Col1a1","Cd34"))
signGenes <- genes %>% dplyr::filter(gene %in% selGenes$gene)

##make a count matrix of signature genes
sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
cntMat <- rowSums(t(as.matrix(
    sceSub@assays@data$logcounts)))/nrow(signGenes)
sceSub$sign <- cntMat
sceSub$sign2 <- sceSub$sign
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, 3))
sceSub$sign2[which(sceSub$sign > 3)] <- 3
##check max and min values
max(sceSub$sign)
[1] 3.508908
plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc +
  theme(legend.position = "none")

plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc

selGenes <- data.frame(gene=c("Fbln5","Eln","Actg2","Acta2","Myh11"))
signGenes <- genes %>% dplyr::filter(gene %in% selGenes$gene)

##make a count matrix of signature genes
sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
cntMat <- rowSums(t(as.matrix(
    sceSub@assays@data$logcounts)))/nrow(signGenes)
sceSub$sign <- cntMat
sceSub$sign2 <- sceSub$sign
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, 3))
sceSub$sign2[which(sceSub$sign > 3)] <- 3
##check max and min values
max(sceSub$sign)
[1] 3.928997
plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc +
  theme(legend.position = "none")

plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc

selGenes <- data.frame(gene=c("Fbln5","Eln","Actg2","Acta2","Myh11","Mfap5","Gsn","Fndc1","Col1a1","Cd34"))
signGenes <- genes %>% dplyr::filter(gene %in% selGenes$gene)

##make a count matrix of signature genes
sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
cntMat <- rowSums(t(as.matrix(
    sceSub@assays@data$logcounts)))/nrow(signGenes)
sceSub$sign <- cntMat
sceSub$sign2 <- sceSub$sign
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, 2.5))
sceSub$sign2[which(sceSub$sign > 2.5)] <- 2.5
##check max and min values
max(sceSub$sign)
[1] 2.554444
plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc +
  theme(legend.position = "none")

plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc

selGenes <- data.frame(gene=c("Mki67", "Ccna2", "Cdca8", "Prc1", "Aurkb"))
signGenes <- genes %>% dplyr::filter(gene %in% selGenes$gene)

##make a count matrix of signature genes
sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
cntMat <- rowSums(t(as.matrix(
    sceSub@assays@data$logcounts)))/nrow(signGenes)
sceSub$sign <- cntMat
sceSub$sign2 <- sceSub$sign
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, 2))
sceSub$sign2[which(sceSub$sign > 2)] <- 2
##check max and min values
max(sceSub$sign)
[1] 2.088394
plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc +
  theme(legend.position = "none")

plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc

GSEA

calculate marker genes

## calculate marker genes
DefaultAssay(object = seuratE18EYFPv2.int) <- "RNA"
Idents(seuratE18EYFPv2.int) <- seuratE18EYFPv2.int$label
levels(seuratE18EYFPv2.int)

markerGenes <- FindAllMarkers(seuratE18EYFPv2.int, only.pos=T) %>% 
  dplyr::filter(p_val_adj < 0.01) 
#save table
write.table(markerGenes, 
            file= paste0(basedir, "/data/markerGenes_E18_EYFPpos_integrated_fil_label.txt",
            sep="\t",
            quote=F,
            row.names=F,
            col.names=T)

run GSEA on cluster2

## load marker genes
markerGenes <- read.delim(paste0(basedir, "/data/markerGenes_E18_EYFPpos_integrated_fil_label.txt"), header = TRUE, sep = "\t")

markerProlif <- dplyr::filter(markerGenes, cluster == "cluster2")
markerProlif <- markerProlif%>%
  mutate(Gene=gsub("^.*\\.", "", gene))  %>%
  mutate(EnsID=gsub("\\..*","", gene))

##GSEA 
ego <- enrichGO(gene = unique(markerProlif$EnsID),
                          OrgDb = org.Mm.eg.db,
                          keyType = 'ENSEMBL',
                          ont = "BP",
                          pAdjustMethod = "BH",
                          pvalueCutoff = 0.05,
                          qvalueCutoff = 0.05)
ego <- setReadable(ego, OrgDb = org.Mm.eg.db)
dotplot(ego, showCategory=30)

run GSEA on cluster3

markerProlif <- dplyr::filter(markerGenes, cluster == "cluster3")
markerProlif <- markerProlif%>%
  mutate(Gene=gsub("^.*\\.", "", gene))  %>%
  mutate(EnsID=gsub("\\..*","", gene))

##GSEA Gran
ego <- enrichGO(gene = unique(markerProlif$EnsID),
                          OrgDb = org.Mm.eg.db,
                          keyType = 'ENSEMBL',
                          ont = "BP",
                          pAdjustMethod = "BH",
                          pvalueCutoff = 0.05,
                          qvalueCutoff = 0.05)
ego <- setReadable(ego, OrgDb = org.Mm.eg.db)
dotplot(ego, showCategory=30)

run GSEA on cluster1

markerProlif <- dplyr::filter(markerGenes, cluster == "cluster1")
markerProlif <- markerProlif%>%
  mutate(Gene=gsub("^.*\\.", "", gene))  %>%
  mutate(EnsID=gsub("\\..*","", gene))

##GSEA Gran
ego <- enrichGO(gene = unique(markerProlif$EnsID),
                          OrgDb = org.Mm.eg.db,
                          keyType = 'ENSEMBL',
                          ont = "BP",
                          pAdjustMethod = "BH",
                          pvalueCutoff = 0.05,
                          qvalueCutoff = 0.05)
ego <- setReadable(ego, OrgDb = org.Mm.eg.db)
dotplot(ego, showCategory=30)

project signatures

stem cell population maintenance

ego1 <- dplyr::filter(ego@result,ego@result$Description=="stem cell population maintenance")
g1 <- ego1$geneID
Str <-(g1)
StrSub <- strsplit(Str, "/")
df <- as.data.frame(StrSub)
colnames(df) <- c("gene")

##make a count matrix of signature genes
signGenes <- genes %>% dplyr::filter(gene %in% df$gene)

sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
cntMat <- rowSums(t(as.matrix(
    sceSub@assays@data$logcounts)))/nrow(signGenes)
sceSub$sign <- cntMat
sceSub$sign2 <- sceSub$sign
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, 0.6))
sceSub$sign2[which(sceSub$sign > 0.6)] <- 0.6
sceSub$sign2[which(sceSub$sign < 0)] <- 0
##check max and min values
max(sceSub$sign)
[1] 0.6425939
min(sceSub$sign)
[1] 0.1083498
plotUMAP(sceSub, colour_by = "sign2") + sc +
  theme(legend.position = "none", point_size = 1)

plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc

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 stem cell population maintenance

sig <- markerGenes %>% mutate(geneID = gene) %>% dplyr::filter(cluster == "cluster1") %>%
  mutate(gene=gsub(".*\\.", "",  geneID)) %>% dplyr::filter(gene %in% df$gene)

grpCnt <- sig %>% group_by(cluster) %>% summarise(cnt=n())
ordVec <- levels(seuratE18EYFPv2.int)

pOut <- avgHeatmap(seurat = seuratE18EYFPv2.int, selGenes = sig,
                  colVecIdent = colLab, 
                  ordVec=ordVec,
                  gapVecR=NULL, gapVecC=NULL,cc=T,
                  cr=F, condCol=F)

positive regulation of stem cell population maintenance

ego1 <- dplyr::filter(ego@result,ego@result$Description=="positive regulation of stem cell population maintenance")
g1 <- ego1$geneID
Str <-(g1)
StrSub <- strsplit(Str, "/")
df <- as.data.frame(StrSub)
colnames(df) <- c("gene")

##make a count matrix of signature genes
signGenes <- genes %>% dplyr::filter(gene %in% df$gene)

sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
cntMat <- rowSums(t(as.matrix(
    sceSub@assays@data$logcounts)))/nrow(signGenes)
sceSub$sign <- cntMat
sceSub$sign2 <- sceSub$sign
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, 0.8))
sceSub$sign2[which(sceSub$sign > 0.8)] <- 0.8
sceSub$sign2[which(sceSub$sign < 0)] <- 0
##check max and min values
max(sceSub$sign)
[1] 0.8026154
min(sceSub$sign)
[1] 0
plotUMAP(sceSub, colour_by = "sign2") + sc +
  theme(legend.position = "none", point_size = 1)

plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc

heatmap positive regulation of stem cell population maintenance

sig <- markerGenes %>% mutate(geneID = gene) %>% dplyr::filter(cluster == "cluster1") %>%
  mutate(gene=gsub(".*\\.", "",  geneID)) %>% dplyr::filter(gene %in% df$gene)

grpCnt <- sig %>% group_by(cluster) %>% summarise(cnt=n())
ordVec <- levels(seuratE18EYFPv2.int)

pOut <- avgHeatmap(seurat = seuratE18EYFPv2.int, selGenes = sig,
                  colVecIdent = colLab, 
                  ordVec=ordVec,
                  gapVecR=NULL, gapVecC=NULL,cc=T,
                  cr=F, condCol=F)

stem cell proliferation

ego1 <- dplyr::filter(ego@result,ego@result$Description=="stem cell proliferation")
g1 <- ego1$geneID
Str <-(g1)
StrSub <- strsplit(Str, "/")
df <- as.data.frame(StrSub)
colnames(df) <- c("gene")

##make a count matrix of signature genes
signGenes <- genes %>% dplyr::filter(gene %in% df$gene)

sceSub <- sce[which(rownames(sce) %in% signGenes$geneID),]
cntMat <- rowSums(t(as.matrix(
    sceSub@assays@data$logcounts)))/nrow(signGenes)
sceSub$sign <- cntMat
sceSub$sign2 <- sceSub$sign
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, 0.8))
sceSub$sign2[which(sceSub$sign > 0.8)] <- 0.8
sceSub$sign2[which(sceSub$sign < 0)] <- 0
##check max and min values
max(sceSub$sign)
[1] 0.7917599
min(sceSub$sign)
[1] 0
plotUMAP(sceSub, colour_by = "sign2") + sc +
  theme(legend.position = "none", point_size = 1)

plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc

heatmap stem cell proliferation

sig <- markerGenes %>% mutate(geneID = gene) %>% dplyr::filter(cluster == "cluster1") %>%
  mutate(gene=gsub(".*\\.", "",  geneID)) %>% dplyr::filter(gene %in% df$gene)

grpCnt <- sig %>% group_by(cluster) %>% summarise(cnt=n())
ordVec <- levels(seuratE18EYFPv2.int)

pOut <- avgHeatmap(seurat = seuratE18EYFPv2.int, selGenes = sig,
                  colVecIdent = colLab, 
                  ordVec=ordVec,
                  gapVecR=NULL, gapVecC=NULL,cc=T,
                  cr=F, condCol=F)

slingshot

sce <- slingshot(sce, clusterLabels = 'label', reducedDim = 'UMAP',
                 start.clus="cluster1",
                 dist.method="simple", extend = 'n', stretch=0)
clustDat <- data.frame(clustCol=colLab) %>% rownames_to_column(., "cluster")
colDat <- data.frame(cluster=seuratE18EYFPv2.int$label)  %>% left_join(., clustDat, by="cluster") 
plot(reducedDims(sce)$UMAP, col = colDat$clustCol, pch=16, asp = 1)
lines(SlingshotDataSet(sce), lwd=2, type = 'lineages', col = 'black')

plot(reducedDims(sce)$UMAP, col = colDat$clustCol, pch=16, asp = 1)
lines(SlingshotDataSet(sce), lwd=2, col='black')

colors <- colorRampPalette(brewer.pal(11,'YlGnBu'))(100)
plotcol <- colors[cut(slingAvgPseudotime(SlingshotDataSet(sce)), breaks=100)]

plot(reducedDims(sce)$UMAP, col = plotcol, pch=16, asp = 1)
lines(SlingshotDataSet(sce), lwd=2, col='black')

colors <- colorRampPalette(brewer.pal(11,'PuOr')[-6])(100)
plotcol <- colors[cut(slingAvgPseudotime(SlingshotDataSet(sce)), breaks=100)]

plot(reducedDims(sce)$UMAP, col = plotcol, pch=16, asp = 1)
lines(SlingshotDataSet(sce), lwd=2, col='black')

session info

date()
[1] "Tue Jul 15 17:47:55 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               RColorBrewer_1.1-3          here_1.0.1                 
 [4] slingshot_2.12.0            TrajectoryUtils_1.12.0      princurve_2.1.6            
 [7] NCmisc_1.2.0                VennDiagram_1.7.3           futile.logger_1.4.3        
[10] ggupset_0.4.1               gridExtra_2.3               DOSE_3.30.5                
[13] enrichplot_1.24.4           msigdbr_24.1.0              org.Mm.eg.db_3.19.1        
[16] AnnotationDbi_1.66.0        clusterProfiler_4.12.6      multtest_2.60.0            
[19] metap_1.12                  scater_1.32.1               scuttle_1.14.0             
[22] destiny_3.18.0              circlize_0.4.16             muscat_1.18.0              
[25] viridis_0.6.5               viridisLite_0.4.2           lubridate_1.9.4            
[28] forcats_1.0.0               stringr_1.5.1               purrr_1.0.4                
[31] readr_2.1.5                 tidyr_1.3.1                 tibble_3.2.1               
[34] tidyverse_2.0.0             dplyr_1.1.4                 SingleCellExperiment_1.26.0
[37] SummarizedExperiment_1.34.0 Biobase_2.64.0              GenomicRanges_1.56.2       
[40] GenomeInfoDb_1.40.1         IRanges_2.38.1              S4Vectors_0.42.1           
[43] BiocGenerics_0.50.0         MatrixGenerics_1.16.0       matrixStats_1.5.0          
[46] pheatmap_1.0.13             ggpubr_0.6.0                ggplot2_3.5.2              
[49] Seurat_5.3.0                SeuratObject_5.1.0          sp_2.2-0                   
[52] 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           formatR_1.14              colourpicker_1.3.0       
 [55] htmlwidgets_1.6.4         fs_1.6.6                  ggrepel_0.9.6            
 [58] labeling_0.4.3            fANCOVA_0.6-1             SparseArray_1.4.8        
 [61] DESeq2_1.44.0             ranger_0.17.0             DEoptimR_1.1-3-1         
 [64] reticulate_1.42.0         hexbin_1.28.5             zoo_1.8-14               
 [67] XVector_0.44.0            knitr_1.50                ggplot.multistats_1.0.1  
 [70] UCSC.utils_1.0.0          RhpcBLASctl_0.23-42       timechange_0.3.0         
 [73] foreach_1.5.2             patchwork_1.3.0           caTools_1.18.3           
 [76] data.table_1.17.4         ggtree_3.12.0             R.oo_1.27.1              
 [79] RSpectra_0.16-2           irlba_2.3.5.1             fastDummies_1.7.5        
 [82] gridGraphics_0.5-1        lazyeval_0.2.2            yaml_2.3.10              
 [85] survival_3.8-3            scattermore_1.2           crayon_1.5.3             
 [88] RcppAnnoy_0.0.22          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              lambda.r_1.2.4            assertthat_0.2.1         
[121] lme4_1.1-37               digest_0.6.37             numDeriv_2016.8-1.1      
[124] Matrix_1.7-3              farver_2.1.2              tzdb_0.5.0               
[127] remaCor_0.0.18            reshape2_1.4.4            yulab.utils_0.2.0        
[130] glue_1.8.0                cachem_1.1.0              polyclip_1.10-7          
[133] generics_0.1.4            Biostrings_2.72.1         mvtnorm_1.3-3            
[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             futile.options_1.0.1     
[187] bitops_1.0-9              Rdpack_2.6.4              promises_1.3.3           
[190] scatterpie_0.2.4          RSQLite_2.4.0             qvalue_2.36.0            
[193] sandwich_3.1-1            fgsea_1.30.0              DelayedArray_0.30.1      
[196] proxy_0.4-27              GO.db_3.19.1              compiler_4.4.0           
[199] prettyunits_1.2.0         boot_1.3-31               beachmat_2.20.0          
[202] listenv_0.9.1             Rcpp_1.0.14               edgeR_4.2.2              
[205] workflowr_1.7.1           BiocSingular_1.20.0       tensor_1.5               
[208] MASS_7.3-65               progress_1.2.3            BiocParallel_1.38.0      
[211] babelgene_22.9            spatstat.random_3.4-1     R6_2.6.1                 
[214] fastmap_1.2.0             multcomp_1.4-28           fastmatch_1.1-6          
[217] rstatix_0.7.2             vipor_0.4.7               TTR_0.24.4               
[220] ROCR_1.0-11               TFisher_0.2.0             rsvd_1.0.5               
[223] vcd_1.4-13                nnet_7.3-20               gtable_0.3.6             
[226] KernSmooth_2.23-26        miniUI_0.1.2              deldir_2.0-4             
[229] htmltools_0.5.8.1         ggthemes_5.1.0            bit64_4.6.0-1            
[232] spatstat.explore_3.4-3    lifecycle_1.0.4           blme_1.0-6               
[235] nloptr_2.2.1              sass_0.4.10               vctrs_0.6.5              
[238] robustbase_0.99-4-1       spatstat.geom_3.4-1       sn_2.1.1                 
[241] ggfun_0.1.8               future.apply_1.11.3       bslib_0.9.0              
[244] pillar_1.10.2             gplots_3.2.0              pcaMethods_1.96.0        
[247] locfit_1.5-9.12           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               RColorBrewer_1.1-3          here_1.0.1                 
 [4] slingshot_2.12.0            TrajectoryUtils_1.12.0      princurve_2.1.6            
 [7] NCmisc_1.2.0                VennDiagram_1.7.3           futile.logger_1.4.3        
[10] ggupset_0.4.1               gridExtra_2.3               DOSE_3.30.5                
[13] enrichplot_1.24.4           msigdbr_24.1.0              org.Mm.eg.db_3.19.1        
[16] AnnotationDbi_1.66.0        clusterProfiler_4.12.6      multtest_2.60.0            
[19] metap_1.12                  scater_1.32.1               scuttle_1.14.0             
[22] destiny_3.18.0              circlize_0.4.16             muscat_1.18.0              
[25] viridis_0.6.5               viridisLite_0.4.2           lubridate_1.9.4            
[28] forcats_1.0.0               stringr_1.5.1               purrr_1.0.4                
[31] readr_2.1.5                 tidyr_1.3.1                 tibble_3.2.1               
[34] tidyverse_2.0.0             dplyr_1.1.4                 SingleCellExperiment_1.26.0
[37] SummarizedExperiment_1.34.0 Biobase_2.64.0              GenomicRanges_1.56.2       
[40] GenomeInfoDb_1.40.1         IRanges_2.38.1              S4Vectors_0.42.1           
[43] BiocGenerics_0.50.0         MatrixGenerics_1.16.0       matrixStats_1.5.0          
[46] pheatmap_1.0.13             ggpubr_0.6.0                ggplot2_3.5.2              
[49] Seurat_5.3.0                SeuratObject_5.1.0          sp_2.2-0                   
[52] 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           formatR_1.14              colourpicker_1.3.0       
 [55] htmlwidgets_1.6.4         fs_1.6.6                  ggrepel_0.9.6            
 [58] labeling_0.4.3            fANCOVA_0.6-1             SparseArray_1.4.8        
 [61] DESeq2_1.44.0             ranger_0.17.0             DEoptimR_1.1-3-1         
 [64] reticulate_1.42.0         hexbin_1.28.5             zoo_1.8-14               
 [67] XVector_0.44.0            knitr_1.50                ggplot.multistats_1.0.1  
 [70] UCSC.utils_1.0.0          RhpcBLASctl_0.23-42       timechange_0.3.0         
 [73] foreach_1.5.2             patchwork_1.3.0           caTools_1.18.3           
 [76] data.table_1.17.4         ggtree_3.12.0             R.oo_1.27.1              
 [79] RSpectra_0.16-2           irlba_2.3.5.1             fastDummies_1.7.5        
 [82] gridGraphics_0.5-1        lazyeval_0.2.2            yaml_2.3.10              
 [85] survival_3.8-3            scattermore_1.2           crayon_1.5.3             
 [88] RcppAnnoy_0.0.22          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              lambda.r_1.2.4            assertthat_0.2.1         
[121] lme4_1.1-37               digest_0.6.37             numDeriv_2016.8-1.1      
[124] Matrix_1.7-3              farver_2.1.2              tzdb_0.5.0               
[127] remaCor_0.0.18            reshape2_1.4.4            yulab.utils_0.2.0        
[130] glue_1.8.0                cachem_1.1.0              polyclip_1.10-7          
[133] generics_0.1.4            Biostrings_2.72.1         mvtnorm_1.3-3            
[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             futile.options_1.0.1     
[187] bitops_1.0-9              Rdpack_2.6.4              promises_1.3.3           
[190] scatterpie_0.2.4          RSQLite_2.4.0             qvalue_2.36.0            
[193] sandwich_3.1-1            fgsea_1.30.0              DelayedArray_0.30.1      
[196] proxy_0.4-27              GO.db_3.19.1              compiler_4.4.0           
[199] prettyunits_1.2.0         boot_1.3-31               beachmat_2.20.0          
[202] listenv_0.9.1             Rcpp_1.0.14               edgeR_4.2.2              
[205] workflowr_1.7.1           BiocSingular_1.20.0       tensor_1.5               
[208] MASS_7.3-65               progress_1.2.3            BiocParallel_1.38.0      
[211] babelgene_22.9            spatstat.random_3.4-1     R6_2.6.1                 
[214] fastmap_1.2.0             multcomp_1.4-28           fastmatch_1.1-6          
[217] rstatix_0.7.2             vipor_0.4.7               TTR_0.24.4               
[220] ROCR_1.0-11               TFisher_0.2.0             rsvd_1.0.5               
[223] vcd_1.4-13                nnet_7.3-20               gtable_0.3.6             
[226] KernSmooth_2.23-26        miniUI_0.1.2              deldir_2.0-4             
[229] htmltools_0.5.8.1         ggthemes_5.1.0            bit64_4.6.0-1            
[232] spatstat.explore_3.4-3    lifecycle_1.0.4           blme_1.0-6               
[235] nloptr_2.2.1              sass_0.4.10               vctrs_0.6.5              
[238] robustbase_0.99-4-1       spatstat.geom_3.4-1       sn_2.1.1                 
[241] ggfun_0.1.8               future.apply_1.11.3       bslib_0.9.0              
[244] pillar_1.10.2             gplots_3.2.0              pcaMethods_1.96.0        
[247] locfit_1.5-9.12           jsonlite_2.0.0            GetoptLong_1.0.5