Last updated: 2025-07-15

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reprocess

load packages

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/LNmLToRev_allmerged_seurat.rds")
seuratM <- readRDS(fileNam)
table(seuratM$timepoint)

  E18    P7    3w    8w 
42711 44836 29577 23167 
table(seuratM$orig.ident)

       
140291

subset E18

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

  E18 
42711 
seuratA <- JoinLayers(seuratA)

#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: 42711
Number of edges: 1412581

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9485
Number of communities: 15
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 42711
Number of edges: 1412581

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9170
Number of communities: 21
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 42711
Number of edges: 1412581

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9042
Number of communities: 25
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 42711
Number of edges: 1412581

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9329
Number of communities: 17
Elapsed time: 8 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")) %>% 
  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 Ptprc+ cells (cluster #7 and #14)
table(seuratA$RNA_snn_res.0.25)
seuratF <- subset(seuratA, RNA_snn_res.0.25 %in% c("7", "14"), invert = TRUE)
table(seuratF$RNA_snn_res.0.25)

seuratE18fil <- seuratF
remove(seuratF)

rerun after fil

#rerun seurat
seuratE18fil <- NormalizeData (object = seuratE18fil)
seuratE18fil <- FindVariableFeatures(object = seuratE18fil)
seuratE18fil <- ScaleData(object = seuratE18fil, verbose = TRUE)
seuratE18fil <- RunPCA(object=seuratE18fil, npcs = 30, verbose = FALSE)
seuratE18fil <- RunTSNE(object=seuratE18fil, reduction="pca", dims = 1:20)
seuratE18fil <- RunUMAP(object=seuratE18fil, reduction="pca", dims = 1:20)
seuratE18fil <- FindNeighbors(object = seuratE18fil, reduction = "pca", dims= 1:20)

res <- c(0.25, 0.6, 0.8, 0.4)
for (i in 1:length(res)) {
  seuratE18fil <- FindClusters(object = seuratE18fil, resolution = res[i], random.seed = 1234)
}

load object fil

fileNam <- paste0(basedir, "/data/LNmLToRev_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/LNmLToRev_E18fil_seurat.rds"))

subset EYFP expressing cells

seuratSub <- subset(seuratE18fil, Rosa26eyfp.Rosa26eyfp>0)
eyfpPos <- colnames(seuratSub)

seuratE18fil$EYFP <- "neg"
seuratE18fil$EYFP[which(colnames(seuratE18fil)%in%eyfpPos)] <- "pos"
table(seuratE18fil$dataset, seuratE18fil$EYFP)

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_seurat.rds")

load object E18 EYFP+

fileNam <- paste0(basedir, "/data/E18_EYFPv2_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)
}

load object E18 EYFP+ integrated

fileNam <- paste0(basedir, "/data/E18EYFPv2_integrated_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")

features E18 EYFP int

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

selGenes <- data.frame(geneID=c("Rosa26eyfp", "Mki67", "Acta2", "Myh11", "Mcam", "Ccl19", "Cxcl13", "Cd34", "Icam1","Vcam1", "Pi16", "Bmp4", "Fmod", "Adipoq", "Msln", "Kcnn3")) %>% 
  left_join(., genes, by = "geneID") 

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

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$intCluster == "2")] <- "Prolif"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "3")] <- "cluster1"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "4")] <- "cluster4"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "5")] <- "Neuronal1"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "6")] <- "Mesothelial"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "7")] <- "Neuronal2"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "8")] <- "cluster5"
table(seuratE18EYFPv2.int$label)

   cluster1    cluster2    cluster3    cluster4    cluster5 Mesothelial   Neuronal1   Neuronal2 
        846        3781        1768         709         126         504         613         235 
     Prolif 
       1557 
##order
seuratE18EYFPv2.int$label <- factor(seuratE18EYFPv2.int$label, levels = c("cluster1", "cluster2", "cluster3", "cluster4", "cluster5", "Neuronal1","Neuronal2", "Mesothelial", "Prolif"))
table(seuratE18EYFPv2.int$label)

   cluster1    cluster2    cluster3    cluster4    cluster5   Neuronal1   Neuronal2 Mesothelial 
        846        3781        1768         709         126         613         235         504 
     Prolif 
       1557 
colLab <- c("#900C3F","#b66e8d", "#003C67FF",
            "#e3953d", "#714542", "#b6856e", "lightblue","grey", "black")
names(colLab) <- c("cluster1", "cluster2", "cluster3", "cluster4", "cluster5", "Neuronal1","Neuronal2", "Mesothelial", "Prolif")

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")

dotplot FRC marker E18 EYFP+ int

seurat_markers <- data.frame(gene=c("Fcgr2b","Fcer2a","Cr2","Cxcl13",
                                    "Slc7a11", "Ccl19", 
                                    "Ccl21a", "Fmod", "Grem1", "Bmp4",
                                    "Tnfsf11", "Fbn2",
                                    "Pltp" ,"C1rb", "Lepr", "Ptn",
                                     "Nr4a1", "Cxcl10", "Cxcl9", 
                                    "F3", "Fbln1", "Gdf10", "Adamtsl1", 
                                    "Col15a1",  "Cd34",
                                    "Igfbp6", "Pi16", "Thy1", "Dpp4", "Sema3c", 
                                    "Acta2", "Myh11", "Mcam", "Itga7", "Esam", "Rgs4", "Adipoq", "Mki67", "Msln", "Kcnn3", "Tcf21"
                                    ))

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("")

saveRDS(seuratE18EYFPv2.int, file=paste0(basedir,"/data/E18EYFPv2_integrated_seurat.rds")

subset FRCs and rerun

table(seuratE18EYFPv2.int$label)

   cluster1    cluster2    cluster3    cluster4    cluster5   Neuronal1   Neuronal2 Mesothelial 
        846        3781        1768         709         126         613         235         504 
     Prolif 
       1557 
seuratE18EYFPv2.int <- subset(seuratE18EYFPv2.int, label %in% c("Neuronal1", "Neuronal2", "Mesothelial"), invert = TRUE)
table(seuratE18EYFPv2.int$label)

cluster1 cluster2 cluster3 cluster4 cluster5   Prolif 
     846     3781     1768      709      126     1557 
## 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: 8787
Number of edges: 284718

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9414
Number of communities: 5
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 8787
Number of edges: 284718

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8396
Number of communities: 11
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 8787
Number of edges: 284718

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8161
Number of communities: 13
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 8787
Number of edges: 284718

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8664
Number of communities: 10
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 8787
Number of edges: 284718

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8921
Number of communities: 8
Elapsed time: 1 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$intCluster == "0")] <- "cluster3"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "1")] <- "cluster1"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "2")] <- "cluster2"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "3")] <- "cluster4"
seuratE18EYFPv2.int$label[which(seuratE18EYFPv2.int$intCluster == "4")] <- "cluster5"
table(seuratE18EYFPv2.int$label)

cluster1 cluster2 cluster3 cluster4 cluster5 
    1523      843     5735      563      123 
##order
seuratE18EYFPv2.int$label <- factor(seuratE18EYFPv2.int$label, levels = c("cluster2", "cluster3", "cluster4", "cluster5", "cluster1"))
table(seuratE18EYFPv2.int$label)

cluster2 cluster3 cluster4 cluster5 cluster1 
     843     5735      563      123     1523 
colLab <- c("#900C3F","#b66e8d", "#003C67FF",
            "#e3953d", "#714542", "#b6856e")
names(colLab) <- c("cluster2", "cluster3", "cluster1", "cluster4", "cluster5")

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")

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 to sce

## 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"))

signatures

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] 2.88562
min(sceSub$sign)
[1] 0
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] 3.469675
min(sceSub$sign)
[1] 0
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] 4.23627
plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc +
  theme(legend.position = "none")

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

plot signature 3/4 combined

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] 2.747664
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] 2.106508
plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc +
  theme(legend.position = "none")

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

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)
})

subset Ccl19 positive cells

seuratCcl19 <- subset(seuratE18EYFPv2.int, ENSMUSG00000071005.Ccl19 > 0)
table(seuratE18EYFPv2.int$orig.ident)

     
8787 
table(seuratCcl19$orig.ident)

     
1023

rerun Ccl19 positive only

## rerun seurat
DefaultAssay(object = seuratCcl19) <- "integrated"
seuratCcl19 <- ScaleData(object = seuratCcl19, verbose = FALSE,
                         features = rownames(seuratCcl19))
seuratCcl19 <- RunPCA(object = seuratCcl19, npcs = 20, verbose = FALSE)
seuratCcl19 <- RunTSNE(object = seuratCcl19, recuction = "pca", dims = 1:20)
seuratCcl19 <- RunUMAP(object = seuratCcl19, recuction = "pca", dims = 1:20)

seuratCcl19 <- FindNeighbors(object = seuratCcl19, reduction = "pca", dims = 1:20)
res <- c(0.1, 0.6, 0.8, 0.4, 0.25)
for (i in 1:length(res)){
  seuratCcl19 <- FindClusters(object = seuratCcl19, resolution = res[i],
                              random.seed = 1234)
}
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1023
Number of edges: 35579

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9211
Number of communities: 2
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1023
Number of edges: 35579

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7329
Number of communities: 5
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1023
Number of edges: 35579

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6914
Number of communities: 5
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1023
Number of edges: 35579

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7876
Number of communities: 4
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1023
Number of edges: 35579

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8482
Number of communities: 3
Elapsed time: 0 seconds
DefaultAssay(object = seuratCcl19) <- "RNA"
Idents(seuratCcl19) <- seuratCcl19$integrated_snn_res.0.25

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(seuratCcl19$integrated_snn_res.0.25))]
names(colPal) <- unique(seuratCcl19$integrated_snn_res.0.25)

dimplots Ccl19+ cells

clustering

DimPlot(seuratCcl19, 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")

assign label Ccl19+ cells

seuratCcl19$label <- "label"
seuratCcl19$label[which(seuratCcl19$integrated_snn_res.0.25 == "0")] <- "cluster2"
seuratCcl19$label[which(seuratCcl19$integrated_snn_res.0.25 == "1")] <- "cluster1"
seuratCcl19$label[which(seuratCcl19$integrated_snn_res.0.25 == "2")] <- "cluster3"
table(seuratCcl19$label)

cluster1 cluster2 cluster3 
     266      597      160 
##order
seuratCcl19$label <- factor(seuratCcl19$label, levels = c("cluster2", "cluster3","cluster1"))
table(seuratCcl19$label)

cluster2 cluster3 cluster1 
     597      160      266 
colLab <- c("#900C3F","#b66e8d", "#003C67FF")
names(colLab) <- c("cluster2", "cluster3", "cluster1")

label

DimPlot(seuratCcl19, 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(seuratCcl19, reduction = "umap", group.by = "label", pt.size=0.5,
        cols = colLab, shuffle = T)+
  theme_void()

DimPlot(seuratCcl19, reduction = "umap", group.by = "label", pt.size=0.5,
        cols = colLab, shuffle = T)+
  theme_void() + 
  theme(legend.position = "none")

featrue plot Ccl19

genes <- data.frame(gene=rownames(seuratCcl19)) %>% 
    mutate(geneID=gsub("^.*\\.", "", gene)) 

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

pList <- sapply(selGenes$gene, function(x){
  p <- FeaturePlot(seuratCcl19, reduction = "umap", 
            features = x, 
            cols=c("lightgrey","darkred"),
            order = FALSE)+
  theme(legend.position="right")
  plot(p)
})

signatures Ccl19+ cells

##convert seurat object to sce object
##exteract logcounts
logcounts <- GetAssayData(seuratCcl19, assay = "RNA", slot = "data")
counts <- GetAssayData(seuratCcl19, assay = "RNA", slot = "counts")
##extract reduced dims from integrated assay
pca <- Embeddings(seuratCcl19, reduction = "pca")
umap <- Embeddings(seuratCcl19, reduction = "umap")
##create sce object
sce <- SingleCellExperiment(assays =list (
  counts = counts,
  logcounts = logcounts
),
colData = seuratCcl19@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$sign
sc <- scale_colour_gradientn(colours = pal(100), limits=c(0, 2.5))
sceSub$sign2[which(sceSub$sign > 2.5)] <- 2.5
sceSub$sign2[which(sceSub$sign < 0)] <- 0
##check max and min values
max(sceSub$sign)
[1] 2.88562
min(sceSub$sign)
[1] 0.1052551
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$sign > 2.5)] <- 2.5
sceSub$sign2[which(sceSub$sign < 0)] <- 0
##check max and min values
max(sceSub$sign)
[1] 2.532537
min(sceSub$sign)
[1] 0
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$sign > 2)] <- 2
sceSub$sign2[which(sceSub$sign < 0)] <- 0
##check max and min values
max(sceSub$sign)
[1] 1.967789
min(sceSub$sign)
[1] 0
plotUMAP(sceSub, colour_by = "sign2", point_size = 1) + sc +
  theme(legend.position = "none")

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

session info

date()
[1] "Tue Jul 15 14:49:20 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               here_1.0.1                  slingshot_2.12.0           
 [4] TrajectoryUtils_1.12.0      princurve_2.1.6             NCmisc_1.2.0               
 [7] VennDiagram_1.7.3           futile.logger_1.4.3         ggupset_0.4.1              
[10] gridExtra_2.3               DOSE_3.30.5                 enrichplot_1.24.4          
[13] msigdbr_24.1.0              org.Mm.eg.db_3.19.1         AnnotationDbi_1.66.0       
[16] clusterProfiler_4.12.6      multtest_2.60.0             metap_1.12                 
[19] scater_1.32.1               scuttle_1.14.0              destiny_3.18.0             
[22] circlize_0.4.16             muscat_1.18.0               viridis_0.6.5              
[25] viridisLite_0.4.2           lubridate_1.9.4             forcats_1.0.0              
[28] stringr_1.5.1               purrr_1.0.4                 readr_2.1.5                
[31] tidyr_1.3.1                 tibble_3.2.1                tidyverse_2.0.0            
[34] dplyr_1.1.4                 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[37] Biobase_2.64.0              GenomicRanges_1.56.2        GenomeInfoDb_1.40.1        
[40] IRanges_2.38.1              S4Vectors_0.42.1            BiocGenerics_0.50.0        
[43] MatrixGenerics_1.16.0       matrixStats_1.5.0           pheatmap_1.0.13            
[46] ggpubr_0.6.0                ggplot2_3.5.2               Seurat_5.3.0               
[49] SeuratObject_5.1.0          sp_2.2-0                    runSeurat3_0.1.0           
[52] 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          RColorBrewer_1.1-3        progressr_0.15.1         
 [91] tweenr_2.0.3              later_1.4.2               ggridges_0.5.6           
 [94] codetools_0.2-20          GlobalOptions_0.1.2       aod_1.3.3                
 [97] KEGGREST_1.44.1           Rtsne_0.17                shape_1.4.6.1            
[100] limma_3.60.6              pkgconfig_2.0.3           TMB_1.9.17               
[103] spatstat.univar_3.1-3     mathjaxr_1.8-0            EnvStats_3.1.0           
[106] aplot_0.2.5               scatterplot3d_0.3-44      ape_5.8-1                
[109] spatstat.sparse_3.1-0     xtable_1.8-4              car_3.1-3                
[112] plyr_1.8.9                httr_1.4.7                rbibutils_2.3            
[115] tools_4.4.0               globals_0.18.0            beeswarm_0.4.0           
[118] broom_1.0.8               nlme_3.1-168              lambda.r_1.2.4           
[121] assertthat_0.2.1          lme4_1.1-37               digest_0.6.37            
[124] numDeriv_2016.8-1.1       Matrix_1.7-3              farver_2.1.2             
[127] tzdb_0.5.0                remaCor_0.0.18            reshape2_1.4.4           
[130] yulab.utils_0.2.0         glue_1.8.0                cachem_1.1.0             
[133] polyclip_1.10-7           generics_0.1.4            Biostrings_2.72.1        
[136] mvtnorm_1.3-3             parallelly_1.45.0         mnormt_2.1.1             
[139] statmod_1.5.0             RcppHNSW_0.6.0            ScaledMatrix_1.12.0      
[142] carData_3.0-5             minqa_1.2.8               pbapply_1.7-2            
[145] httr2_1.1.2               spam_2.11-1               gson_0.1.0               
[148] graphlayouts_1.2.2        gtools_3.9.5              ggsignif_0.6.4           
[151] RcppEigen_0.3.4.0.2       shiny_1.10.0              GenomeInfoDbData_1.2.12  
[154] glmmTMB_1.1.11            R.utils_2.13.0            memoise_2.0.1            
[157] rmarkdown_2.29            scales_1.4.0              R.methodsS3_1.8.2        
[160] RANN_2.6.2                Cairo_1.6-2               spatstat.data_3.1-6      
[163] rstudioapi_0.17.1         cluster_2.1.8.1           mutoss_0.1-13            
[166] spatstat.utils_3.1-4      hms_1.1.3                 fitdistrplus_1.2-2       
[169] cowplot_1.1.3             colorspace_2.1-1          rlang_1.1.6              
[172] DelayedMatrixStats_1.26.0 sparseMatrixStats_1.16.0  xts_0.14.1               
[175] dotCall64_1.2             shinydashboard_0.7.3      ggforce_0.4.2            
[178] laeken_0.5.3              mgcv_1.9-3                xfun_0.52                
[181] e1071_1.7-16              TH.data_1.1-3             iterators_1.0.14         
[184] abind_1.4-8               GOSemSim_2.30.2           treeio_1.28.0            
[187] futile.options_1.0.1      bitops_1.0-9              Rdpack_2.6.4             
[190] promises_1.3.3            scatterpie_0.2.4          RSQLite_2.4.0            
[193] qvalue_2.36.0             sandwich_3.1-1            fgsea_1.30.0             
[196] DelayedArray_0.30.1       proxy_0.4-27              GO.db_3.19.1             
[199] compiler_4.4.0            prettyunits_1.2.0         boot_1.3-31              
[202] beachmat_2.20.0           listenv_0.9.1             Rcpp_1.0.14              
[205] edgeR_4.2.2               workflowr_1.7.1           BiocSingular_1.20.0      
[208] tensor_1.5                MASS_7.3-65               progress_1.2.3           
[211] BiocParallel_1.38.0       babelgene_22.9            spatstat.random_3.4-1    
[214] R6_2.6.1                  fastmap_1.2.0             multcomp_1.4-28          
[217] fastmatch_1.1-6           rstatix_0.7.2             vipor_0.4.7              
[220] TTR_0.24.4                ROCR_1.0-11               TFisher_0.2.0            
[223] rsvd_1.0.5                vcd_1.4-13                nnet_7.3-20              
[226] gtable_0.3.6              KernSmooth_2.23-26        miniUI_0.1.2             
[229] deldir_2.0-4              htmltools_0.5.8.1         ggthemes_5.1.0           
[232] bit64_4.6.0-1             spatstat.explore_3.4-3    lifecycle_1.0.4          
[235] blme_1.0-6                nloptr_2.2.1              sass_0.4.10              
[238] vctrs_0.6.5               robustbase_0.99-4-1       spatstat.geom_3.4-1      
[241] sn_2.1.1                  ggfun_0.1.8               future.apply_1.11.3      
[244] bslib_0.9.0               pillar_1.10.2             gplots_3.2.0             
[247] pcaMethods_1.96.0         locfit_1.5-9.12           jsonlite_2.0.0           
[250] 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               here_1.0.1                  slingshot_2.12.0           
 [4] TrajectoryUtils_1.12.0      princurve_2.1.6             NCmisc_1.2.0               
 [7] VennDiagram_1.7.3           futile.logger_1.4.3         ggupset_0.4.1              
[10] gridExtra_2.3               DOSE_3.30.5                 enrichplot_1.24.4          
[13] msigdbr_24.1.0              org.Mm.eg.db_3.19.1         AnnotationDbi_1.66.0       
[16] clusterProfiler_4.12.6      multtest_2.60.0             metap_1.12                 
[19] scater_1.32.1               scuttle_1.14.0              destiny_3.18.0             
[22] circlize_0.4.16             muscat_1.18.0               viridis_0.6.5              
[25] viridisLite_0.4.2           lubridate_1.9.4             forcats_1.0.0              
[28] stringr_1.5.1               purrr_1.0.4                 readr_2.1.5                
[31] tidyr_1.3.1                 tibble_3.2.1                tidyverse_2.0.0            
[34] dplyr_1.1.4                 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[37] Biobase_2.64.0              GenomicRanges_1.56.2        GenomeInfoDb_1.40.1        
[40] IRanges_2.38.1              S4Vectors_0.42.1            BiocGenerics_0.50.0        
[43] MatrixGenerics_1.16.0       matrixStats_1.5.0           pheatmap_1.0.13            
[46] ggpubr_0.6.0                ggplot2_3.5.2               Seurat_5.3.0               
[49] SeuratObject_5.1.0          sp_2.2-0                    runSeurat3_0.1.0           
[52] 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          RColorBrewer_1.1-3        progressr_0.15.1         
 [91] tweenr_2.0.3              later_1.4.2               ggridges_0.5.6           
 [94] codetools_0.2-20          GlobalOptions_0.1.2       aod_1.3.3                
 [97] KEGGREST_1.44.1           Rtsne_0.17                shape_1.4.6.1            
[100] limma_3.60.6              pkgconfig_2.0.3           TMB_1.9.17               
[103] spatstat.univar_3.1-3     mathjaxr_1.8-0            EnvStats_3.1.0           
[106] aplot_0.2.5               scatterplot3d_0.3-44      ape_5.8-1                
[109] spatstat.sparse_3.1-0     xtable_1.8-4              car_3.1-3                
[112] plyr_1.8.9                httr_1.4.7                rbibutils_2.3            
[115] tools_4.4.0               globals_0.18.0            beeswarm_0.4.0           
[118] broom_1.0.8               nlme_3.1-168              lambda.r_1.2.4           
[121] assertthat_0.2.1          lme4_1.1-37               digest_0.6.37            
[124] numDeriv_2016.8-1.1       Matrix_1.7-3              farver_2.1.2             
[127] tzdb_0.5.0                remaCor_0.0.18            reshape2_1.4.4           
[130] yulab.utils_0.2.0         glue_1.8.0                cachem_1.1.0             
[133] polyclip_1.10-7           generics_0.1.4            Biostrings_2.72.1        
[136] mvtnorm_1.3-3             parallelly_1.45.0         mnormt_2.1.1             
[139] statmod_1.5.0             RcppHNSW_0.6.0            ScaledMatrix_1.12.0      
[142] carData_3.0-5             minqa_1.2.8               pbapply_1.7-2            
[145] httr2_1.1.2               spam_2.11-1               gson_0.1.0               
[148] graphlayouts_1.2.2        gtools_3.9.5              ggsignif_0.6.4           
[151] RcppEigen_0.3.4.0.2       shiny_1.10.0              GenomeInfoDbData_1.2.12  
[154] glmmTMB_1.1.11            R.utils_2.13.0            memoise_2.0.1            
[157] rmarkdown_2.29            scales_1.4.0              R.methodsS3_1.8.2        
[160] RANN_2.6.2                Cairo_1.6-2               spatstat.data_3.1-6      
[163] rstudioapi_0.17.1         cluster_2.1.8.1           mutoss_0.1-13            
[166] spatstat.utils_3.1-4      hms_1.1.3                 fitdistrplus_1.2-2       
[169] cowplot_1.1.3             colorspace_2.1-1          rlang_1.1.6              
[172] DelayedMatrixStats_1.26.0 sparseMatrixStats_1.16.0  xts_0.14.1               
[175] dotCall64_1.2             shinydashboard_0.7.3      ggforce_0.4.2            
[178] laeken_0.5.3              mgcv_1.9-3                xfun_0.52                
[181] e1071_1.7-16              TH.data_1.1-3             iterators_1.0.14         
[184] abind_1.4-8               GOSemSim_2.30.2           treeio_1.28.0            
[187] futile.options_1.0.1      bitops_1.0-9              Rdpack_2.6.4             
[190] promises_1.3.3            scatterpie_0.2.4          RSQLite_2.4.0            
[193] qvalue_2.36.0             sandwich_3.1-1            fgsea_1.30.0             
[196] DelayedArray_0.30.1       proxy_0.4-27              GO.db_3.19.1             
[199] compiler_4.4.0            prettyunits_1.2.0         boot_1.3-31              
[202] beachmat_2.20.0           listenv_0.9.1             Rcpp_1.0.14              
[205] edgeR_4.2.2               workflowr_1.7.1           BiocSingular_1.20.0      
[208] tensor_1.5                MASS_7.3-65               progress_1.2.3           
[211] BiocParallel_1.38.0       babelgene_22.9            spatstat.random_3.4-1    
[214] R6_2.6.1                  fastmap_1.2.0             multcomp_1.4-28          
[217] fastmatch_1.1-6           rstatix_0.7.2             vipor_0.4.7              
[220] TTR_0.24.4                ROCR_1.0-11               TFisher_0.2.0            
[223] rsvd_1.0.5                vcd_1.4-13                nnet_7.3-20              
[226] gtable_0.3.6              KernSmooth_2.23-26        miniUI_0.1.2             
[229] deldir_2.0-4              htmltools_0.5.8.1         ggthemes_5.1.0           
[232] bit64_4.6.0-1             spatstat.explore_3.4-3    lifecycle_1.0.4          
[235] blme_1.0-6                nloptr_2.2.1              sass_0.4.10              
[238] vctrs_0.6.5               robustbase_0.99-4-1       spatstat.geom_3.4-1      
[241] sn_2.1.1                  ggfun_0.1.8               future.apply_1.11.3      
[244] bslib_0.9.0               pillar_1.10.2             gplots_3.2.0             
[247] pcaMethods_1.96.0         locfit_1.5-9.12           jsonlite_2.0.0           
[250] GetoptLong_1.0.5