integrate data from adult mLN and iLN

Last updated: 2025-07-14

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Rmd	0ff885c	angeldemartin	2025-07-11	july11
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preprocessing

load packages

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

load object all

basedir <- here()
fileNam <- paste0(basedir, "/data/LNmLToRev_allmerged_seurat.rds")
seuratM <- readRDS(fileNam)
table(seuratM$orig.ident)

subset adult

seuratA <- subset(seuratM, timepoint == "8w")
table(seuratM$timepoint)


  E18    P7    3w    8w 
42711 44836 29577 23167

table(seuratA$timepoint)


   8w 
23167

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

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

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 23167
Number of edges: 747906

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9267
Number of communities: 12
Elapsed time: 4 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 23167
Number of edges: 747906

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8790
Number of communities: 17
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 23167
Number of edges: 747906

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8611
Number of communities: 20
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 23167
Number of edges: 747906

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9025
Number of communities: 15
Elapsed time: 5 seconds

plot umaps

clustering

colPal <- c("#DAF7A6", "#FFC300", "#FF5733", "#C70039", "#900C3F", "#b66e8d",
            "#61a4ba", "#6178ba", "#54a87f", "#25328a", "#b6856e",
            "#ba6161", "#20714a", "#0073C2FF", "#EFC000FF", "#868686FF", 
            "#CD534CFF","#7AA6DCFF", "#003C67FF", "#8F7700FF", "#3B3B3BFF",
            "#A73030FF", "#4A6990FF")[1:length(unique(seuratM$RNA_snn_res.0.25))]
names(colPal) <- unique(seuratM$RNA_snn_res.0.25)

DimPlot(seuratA, reduction = "umap", group.by = "RNA_snn_res.0.25" ,
        pt.size = 0.1, label = T, shuffle = T, cols = colPal) +
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("umap1") +
  ylab("umap2")

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

location

collocation <- c("#61baba", "#ba6161")
names(collocation) <- c("iLN", "mLN")
DimPlot(seuratA, reduction = "umap", group.by = "location", cols = collocation,
        pt.size = 0.1, label = T, shuffle = T) +
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("umap1") +
  ylab("umap2")

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

calculate cluster marker genes pre filter

Idents(seuratA) <- seuratA$RNA_snn_res.0.25
levels(seuratA)

markerGenes <- FindAllMarkers(seuratA, only.pos=T) %>% 
  dplyr::filter(p_val_adj < 0.01)

features pre filter

selGenesViolin <- c("ENSMUSG00000020717.Pecam1", "ENSMUSG00000026395.Ptprc", "ENSMUSG00000031004.Mki67")

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

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filter object

## filter Pecam1 (cluster #6) and Ptprc (cluster #9 and #10) Mki67 (#8) and pancreatic cells (#11)
table(seuratA$RNA_snn_res.0.25)
seuratF <- subset(seuratA, RNA_snn_res.0.25 %in% c("6", "8" ,"9", "10", "11"), invert = TRUE)
table(seuratF$RNA_snn_res.0.25)

seuratA <- seuratF
remove(seuratF)

rerun seurat after filter

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

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

## save object
saveRDS(seuratA, file=paste0(basedir,"/data/LNmLToRev_adultonly_seurat.rds"))

load object adult

## load object adult only
fileNam <- paste0(basedir, "/data/LNmLToRev_adultonly_seurat.rds")
seuratA <- readRDS(fileNam)
table(seuratA$dataset)


380131_11-11_20250305_Mu_Cxcl13EYFP_Adult_pLN_FRC 380131_12-12_20250305_Mu_Cxcl13EYFP_Adult_mLN_FRC 
                                             5253                                              6419 
 382581_08-8_20250320_Mu_Cxcl13EYFP_Adult_pLN_FRC  382581_09-9_20250320_Mu_Cxcl13EYFP_Adult_mLN_FRC 
                                             7094                                              3522

set color vectors

colLoc <- c("#61baba", "#ba6161")
names(colLoc) <- unique(seuratA$location)

colLab <- c("#42a071", "#900C3F","#b66e8d", "#8F7700FF", "#61a4ba","#003C67FF",
            "#e3953d","#ab5711", "#714542", "#b6856e", "#FFC300")

names(colLab) <- c("FDC", "TRC", "TBRC", "IFRC", "medRC1" , "medRC2",
                   "PRC1", "PRC2", "Pi16+RC", "PRC3", "VSMC")

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

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

plot umaps

clustering

colPal <- c("#DAF7A6", "#FFC300", "#FF5733", "#C70039", "#900C3F", "#b66e8d",
            "#61a4ba", "#6178ba", "#54a87f", "#25328a", "#b6856e",
            "#ba6161", "#20714a", "#0073C2FF", "#EFC000FF", "#868686FF", 
            "#CD534CFF","#7AA6DCFF", "#003C67FF", "#8F7700FF", "#3B3B3BFF",
            "#A73030FF", "#4A6990FF")[1:length(unique(seuratA$RNA_snn_res.0.25))]
names(colPal) <- unique(seuratA$RNA_snn_res.0.25)
DimPlot(seuratA, reduction = "umap", group.by = "RNA_snn_res.0.25", cols = colPal,
        pt.size = 0.1, label = T, shuffle = T) +
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("umap1") +
  ylab("umap2")

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

location

DimPlot(seuratA, reduction = "umap", group.by = "location", cols = collocation,
        pt.size = 0.1, label = T, shuffle = T) +
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("umap1") +
  ylab("umap2")

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

plot features

selGenesViolin <- c("ENSMUSG00000022037.Clu", "ENSMUSG00000094686.Ccl21a",
                    "ENSMUSG00000074934.Grem1", "ENSMUSG00000050069.Grem2",
                    "ENSMUSG00000042436.Mfap4", "ENSMUSG00000071005.Ccl19",
                    "ENSMUSG00000016494.Cd34", "ENSMUSG00000001119.Col6a1",
                    "ENSMUSG00000020241.Col6a2","Rosa26eyfp.Rosa26eyfp", 
                    "ENSMUSG00000023078.Cxcl13", "ENSMUSG00000032135.Mcam")

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

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integrate data across location

Idents(seuratA) <- seuratA$location

seurat.list <- SplitObject(object = seuratA, split.by = "location")
for (i in 1:length(x = seurat.list)) {
    seurat.list[[i]] <- NormalizeData(object = seurat.list[[i]],
                                      verbose = FALSE)
    seurat.list[[i]] <- FindVariableFeatures(object = seurat.list[[i]], 
        selection.method = "vst", nfeatures = 2000, verbose = FALSE)
}

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

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

seuratA.int <- FindNeighbors(object = seuratA.int, reduction = "pca", dims = 1:20)
res <- c(0.6, 0.8, 0.4, 0.25)
for (i in 1:length(res)){
  seuratA.int <- FindClusters(object = seuratA.int, resolution = res[i],
                              random.seed = 1234)
}

load integrated object adult

fileNam <- paste0(basedir, "/data/LNmLToRev_adultonly_seurat.integrated.rds")
seuratA.int <- readRDS(fileNam)

DefaultAssay(object = seuratA.int) <- "RNA"
seuratA.int$intCluster <- seuratA.int$integrated_snn_res.0.4
Idents(seuratA.int) <- seuratA.int$intCluster

colPal <- c("#DAF7A6", "#FFC300", "#FF5733", "#C70039", "#900C3F", "#b66e8d",
            "#61a4ba", "#6178ba", "#54a87f", "#25328a", "#b6856e",
            "#ba6161", "#20714a", "#0073C2FF", "#EFC000FF", "#868686FF", 
            "#CD534CFF","#7AA6DCFF", "#003C67FF", "#8F7700FF", "#3B3B3BFF",
            "#A73030FF", "#4A6990FF")[1:length(unique(seuratA.int$intCluster))]
names(colPal) <- unique(seuratA.int$intCluster)

assign label

seuratA.int$label <- "label"
seuratA.int$label[which(seuratA.int$intCluster == "0")] <- "MedRC/IFRC"
seuratA.int$label[which(seuratA.int$intCluster == "1")] <- "actMedRC"
seuratA.int$label[which(seuratA.int$intCluster == "2")] <- "TBRC"
seuratA.int$label[which(seuratA.int$intCluster == "3")] <- "TRC"
seuratA.int$label[which(seuratA.int$intCluster == "4")] <- "MedRC"
seuratA.int$label[which(seuratA.int$intCluster == "5")] <- "PRC"
seuratA.int$label[which(seuratA.int$intCluster == "6")] <- "FDC/MRC" 
seuratA.int$label[which(seuratA.int$intCluster == "7")] <- "Pi16+RC"
seuratA.int$label[which(seuratA.int$intCluster == "8")] <- "VSMC"

table(seuratA.int$label)


  actMedRC    FDC/MRC      MedRC MedRC/IFRC    Pi16+RC        PRC       TBRC        TRC       VSMC 
      4020       1292       2371       4868        600       2192       3841       2839        265

colLab <- c("#42a071", "#900C3F","#b66e8d", "#61a4ba", "#424671", "#003C67FF",
            "#e3953d", "#714542", "#b6856e", "#FFC300")

names(colLab) <- c("FDC/MRC", "TRC", "TBRC", "MedRC/IFRC", "MedRC" , "actMedRC",
                   "PRC", "Pi16+RC", "VSMC")

saveRDS(seuratA.int, file=paste0(basedir,"/data/LNmLToRev_adultonly_seurat.integrated.rds"))

Dimplot int data

clustering

DimPlot(seuratA.int, reduction = "umap",
        pt.size = 0.1, label = T, shuffle = T, cols = colPal) +
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("umap1") +
  ylab("umap2")

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

label

DimPlot(seuratA.int, reduction = "umap", group.by = "label", cols = colLab)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

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0ff885c	angeldemartin	2025-07-11

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

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

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

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0ff885c	angeldemartin	2025-07-11

label mLN iLN sep

mLN

seuratA.intmLN <- subset(seuratA.int, location == "mLN")

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

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

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

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0ff885c	angeldemartin	2025-07-11

iLN

seuratA.intiLN <- subset(seuratA.int, location == "iLN")

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

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

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

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

label split by location

DimPlot(seuratA.int, reduction = "umap", group.by = "label", cols = colLab,
        split.by = "location")+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

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

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0ff885c	angeldemartin	2025-07-11

location

DimPlot(seuratA.int, reduction = "umap", group.by = "location", cols = colLoc,
        pt.size = 0.1, shuffle = T) +
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("umap1") +
  ylab("umap2")

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

DimPlot(seuratA.int, reduction = "umap", group.by = "location", pt.size=0.5,
        cols = colLoc, split.by = "location", shuffle = T)+
  theme_void()

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

plot features int

selGenesViolin <- c("ENSMUSG00000022037.Clu", "ENSMUSG00000094686.Ccl21a",
                    "ENSMUSG00000074934.Grem1", "ENSMUSG00000050069.Grem2",
                    "ENSMUSG00000042436.Mfap4", "ENSMUSG00000071005.Ccl19",
                    "ENSMUSG00000016494.Cd34", "ENSMUSG00000024011.Pi16", 
                    "ENSMUSG00000001119.Col6a1", "ENSMUSG00000020241.Col6a2",
                    "Rosa26eyfp.Rosa26eyfp", "ENSMUSG00000023078.Cxcl13",
                    "ENSMUSG00000032135.Mcam", "ENSMUSG00000023034.Nr4a1")

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

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cluster characterization

heatmap function

avgHeatmap <- function(seurat, selGenes, colVecIdent, colVecCond=NULL,
                       ordVec=NULL, gapVecR=NULL, gapVecC=NULL,cc=FALSE,
                       cr=FALSE, condCol=FALSE){
  
  selGenes <- selGenes$gene
  
  ## assay data
  clusterAssigned <- as.data.frame(Idents(seurat)) %>%
  dplyr::mutate(cell=rownames(.))
  colnames(clusterAssigned)[1] <- "ident"
  seuratDat <- GetAssayData(seurat)
  
  ## genes of interest
  genes <- data.frame(gene=rownames(seurat)) %>% 
    mutate(geneID=gsub("^.*\\.", "", gene)) %>% filter(geneID %in% selGenes)

  ## matrix with averaged cnts per ident
  logNormExpres <- as.data.frame(t(as.matrix(
    seuratDat[which(rownames(seuratDat) %in% genes$gene),])))
  logNormExpres <- logNormExpres %>% dplyr::mutate(cell=rownames(.)) %>%
    dplyr::left_join(.,clusterAssigned, by=c("cell")) %>%
    dplyr::select(-cell) %>% dplyr::group_by(ident) %>%
    dplyr::summarise_all(mean)
  logNormExpresMa <- logNormExpres %>% dplyr::select(-ident) %>% as.matrix()
  rownames(logNormExpresMa) <- logNormExpres$ident
  logNormExpresMa <- t(logNormExpresMa)
  rownames(logNormExpresMa) <- gsub("^.*?\\.","",rownames(logNormExpresMa))
  
  ## remove genes if they are all the same in all groups
  ind <- apply(logNormExpresMa, 1, sd) == 0
  logNormExpresMa <- logNormExpresMa[!ind,]
  genes <- genes[!ind,]

  ## color columns according to cluster
  annotation_col <- as.data.frame(gsub("(^.*?_)","",
                                       colnames(logNormExpresMa)))%>%
    dplyr::mutate(celltype=gsub("(_.*$)","",colnames(logNormExpresMa)))
  colnames(annotation_col)[1] <- "col1"
  annotation_col <- annotation_col %>%
    dplyr::mutate(cond = gsub(".*_","",col1)) %>%
    dplyr::select(cond, celltype)
  rownames(annotation_col) <- colnames(logNormExpresMa) 

  ann_colors = list(
      cond = colVecCond,
      celltype=colVecIdent)
  if(is.null(ann_colors$cond)){
    annotation_col$cond <- NULL
  }
  
  ## adjust order
  logNormExpresMa <- logNormExpresMa[selGenes,]
  if(is.null(ordVec)){
    ordVec <- levels(seurat)
  }
  logNormExpresMa <- logNormExpresMa[,ordVec]

  ## scaled row-wise
  pheatmap(logNormExpresMa, scale="row" ,treeheight_row = 0, cluster_rows = cr, 
         cluster_cols = cc,
         color = colorRampPalette(c("#2166AC", "#F7F7F7", "#B2182B"))(50),
         annotation_col = annotation_col, cellwidth=15, cellheight=10,
         annotation_colors = ann_colors, gaps_row = gapVecR, gaps_col = gapVecC)
}

heatmap

seuratA.int <- JoinLayers(seuratA.int)
Idents(seuratA.int) <- seuratA.int$intCluster

seuratAint_markers <- FindAllMarkers(seuratA.int, only.pos = T, logfc.threshold = 0.25)

## plot DE genes top 10 avg logFC
markerAll <- seuratAint_markers %>% group_by(cluster) %>% 
  mutate(geneID = gene) %>% top_n(10, avg_log2FC) %>%
  mutate(gene=gsub(".*\\.", "",  geneID)) %>% 
  filter(nchar(gene)>1)

grpCnt <- markerAll %>% group_by(cluster) %>% summarise(cnt=n())
gapR <- data.frame(cluster=unique(markerAll$cluster)) %>% 
  left_join(.,grpCnt, by="cluster") %>% mutate(cumSum=cumsum(cnt)) 
ordVec <- levels(seuratA.int)

pOut <- avgHeatmap(seurat = seuratA.int, selGenes = markerAll,
                  colVecIdent = colPal, 
                  ordVec=ordVec,
                  gapVecR=gapR$cumSum, gapVecC=NULL,cc=T,
                  cr=F, condCol=F)

Version	Author	Date
0ff885c	angeldemartin	2025-07-11

session info

date()

[1] "Mon Jul 14 16:14:15 2025"

sessionInfo()

R version 4.4.0 (2024-04-24)
Platform: x86_64-apple-darwin20
Running under: macOS Ventura 13.7.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Zurich
tzcode source: internal

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] future_1.58.0               NCmisc_1.2.0                ggupset_0.4.1              
 [4] gridExtra_2.3               DOSE_3.30.5                 enrichplot_1.24.4          
 [7] msigdbr_24.1.0              org.Hs.eg.db_3.19.1         AnnotationDbi_1.66.0       
[10] clusterProfiler_4.12.6      multtest_2.60.0             metap_1.12                 
[13] scater_1.32.1               scuttle_1.14.0              destiny_3.18.0             
[16] circlize_0.4.16             muscat_1.18.0               here_1.0.1                 
[19] viridis_0.6.5               viridisLite_0.4.2           lubridate_1.9.4            
[22] forcats_1.0.0               stringr_1.5.1               purrr_1.0.4                
[25] readr_2.1.5                 tidyr_1.3.1                 tibble_3.2.1               
[28] tidyverse_2.0.0             dplyr_1.1.4                 SingleCellExperiment_1.26.0
[31] SummarizedExperiment_1.34.0 Biobase_2.64.0              GenomicRanges_1.56.2       
[34] GenomeInfoDb_1.40.1         IRanges_2.38.1              S4Vectors_0.42.1           
[37] BiocGenerics_0.50.0         MatrixGenerics_1.16.0       matrixStats_1.5.0          
[40] pheatmap_1.0.13             ggpubr_0.6.0                ggplot2_3.5.2              
[43] Seurat_5.3.0                SeuratObject_5.1.0          sp_2.2-0                   
[46] runSeurat3_0.1.0            ExploreSCdataSeurat3_0.1.0 

loaded via a namespace (and not attached):
  [1] igraph_2.1.4              ica_1.0-3                 plotly_4.10.4            
  [4] Formula_1.2-5             zlibbioc_1.50.0           tidyselect_1.2.1         
  [7] bit_4.6.0                 doParallel_1.0.17         clue_0.3-66              
 [10] lattice_0.22-7            rjson_0.2.23              blob_1.2.4               
 [13] S4Arrays_1.4.1            pbkrtest_0.5.4            parallel_4.4.0           
 [16] png_0.1-8                 plotrix_3.8-4             ggplotify_0.1.2          
 [19] cli_3.6.5                 goftest_1.2-3             VIM_6.2.2                
 [22] variancePartition_1.34.0  BiocNeighbors_1.22.0      shadowtext_0.1.4         
 [25] uwot_0.2.3                curl_6.2.3                tidytree_0.4.6           
 [28] mime_0.13                 evaluate_1.0.3            ComplexHeatmap_2.20.0    
 [31] stringi_1.8.7             backports_1.5.0           lmerTest_3.1-3           
 [34] qqconf_1.3.2              httpuv_1.6.16             magrittr_2.0.3           
 [37] rappdirs_0.3.3            splines_4.4.0             ggraph_2.2.1             
 [40] sctransform_0.4.2         ggbeeswarm_0.7.2          DBI_1.2.3                
 [43] jquerylib_0.1.4           smoother_1.3              withr_3.0.2              
 [46] git2r_0.36.2              corpcor_1.6.10            reformulas_0.4.1         
 [49] class_7.3-23              rprojroot_2.0.4           lmtest_0.9-40            
 [52] tidygraph_1.3.1           colourpicker_1.3.0        htmlwidgets_1.6.4        
 [55] fs_1.6.6                  ggrepel_0.9.6             labeling_0.4.3           
 [58] fANCOVA_0.6-1             SparseArray_1.4.8         DESeq2_1.44.0            
 [61] ranger_0.17.0             DEoptimR_1.1-3-1          reticulate_1.42.0        
 [64] hexbin_1.28.5             zoo_1.8-14                XVector_0.44.0           
 [67] knitr_1.50                ggplot.multistats_1.0.1   UCSC.utils_1.0.0         
 [70] RhpcBLASctl_0.23-42       timechange_0.3.0          foreach_1.5.2            
 [73] patchwork_1.3.0           caTools_1.18.3            ggtree_3.12.0            
 [76] data.table_1.17.4         R.oo_1.27.1               RSpectra_0.16-2          
 [79] irlba_2.3.5.1             gridGraphics_0.5-1        fastDummies_1.7.5        
 [82] lazyeval_0.2.2            yaml_2.3.10               survival_3.8-3           
 [85] scattermore_1.2           crayon_1.5.3              RcppAnnoy_0.0.22         
 [88] RColorBrewer_1.1-3        progressr_0.15.1          tweenr_2.0.3             
 [91] later_1.4.2               ggridges_0.5.6            codetools_0.2-20         
 [94] GlobalOptions_0.1.2       aod_1.3.3                 KEGGREST_1.44.1          
 [97] Rtsne_0.17                shape_1.4.6.1             limma_3.60.6             
[100] pkgconfig_2.0.3           TMB_1.9.17                spatstat.univar_3.1-3    
[103] mathjaxr_1.8-0            EnvStats_3.1.0            aplot_0.2.5              
[106] scatterplot3d_0.3-44      ape_5.8-1                 spatstat.sparse_3.1-0    
[109] xtable_1.8-4              car_3.1-3                 plyr_1.8.9               
[112] httr_1.4.7                rbibutils_2.3             tools_4.4.0              
[115] globals_0.18.0            beeswarm_0.4.0            broom_1.0.8              
[118] nlme_3.1-168              assertthat_0.2.1          lme4_1.1-37              
[121] digest_0.6.37             numDeriv_2016.8-1.1       Matrix_1.7-3             
[124] farver_2.1.2              tzdb_0.5.0                remaCor_0.0.18           
[127] reshape2_1.4.4            yulab.utils_0.2.0         glue_1.8.0               
[130] cachem_1.1.0              polyclip_1.10-7           generics_0.1.4           
[133] Biostrings_2.72.1         mvtnorm_1.3-3             presto_1.0.0             
[136] parallelly_1.45.0         mnormt_2.1.1              statmod_1.5.0            
[139] RcppHNSW_0.6.0            ScaledMatrix_1.12.0       carData_3.0-5            
[142] minqa_1.2.8               pbapply_1.7-2             httr2_1.1.2              
[145] spam_2.11-1               gson_0.1.0                graphlayouts_1.2.2       
[148] gtools_3.9.5              ggsignif_0.6.4            RcppEigen_0.3.4.0.2      
[151] shiny_1.10.0              GenomeInfoDbData_1.2.12   glmmTMB_1.1.11           
[154] R.utils_2.13.0            memoise_2.0.1             rmarkdown_2.29           
[157] scales_1.4.0              R.methodsS3_1.8.2         RANN_2.6.2               
[160] Cairo_1.6-2               spatstat.data_3.1-6       rstudioapi_0.17.1        
[163] cluster_2.1.8.1           whisker_0.4.1             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] 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               NCmisc_1.2.0                ggupset_0.4.1              
 [4] gridExtra_2.3               DOSE_3.30.5                 enrichplot_1.24.4          
 [7] msigdbr_24.1.0              org.Hs.eg.db_3.19.1         AnnotationDbi_1.66.0       
[10] clusterProfiler_4.12.6      multtest_2.60.0             metap_1.12                 
[13] scater_1.32.1               scuttle_1.14.0              destiny_3.18.0             
[16] circlize_0.4.16             muscat_1.18.0               here_1.0.1                 
[19] viridis_0.6.5               viridisLite_0.4.2           lubridate_1.9.4            
[22] forcats_1.0.0               stringr_1.5.1               purrr_1.0.4                
[25] readr_2.1.5                 tidyr_1.3.1                 tibble_3.2.1               
[28] tidyverse_2.0.0             dplyr_1.1.4                 SingleCellExperiment_1.26.0
[31] SummarizedExperiment_1.34.0 Biobase_2.64.0              GenomicRanges_1.56.2       
[34] GenomeInfoDb_1.40.1         IRanges_2.38.1              S4Vectors_0.42.1           
[37] BiocGenerics_0.50.0         MatrixGenerics_1.16.0       matrixStats_1.5.0          
[40] pheatmap_1.0.13             ggpubr_0.6.0                ggplot2_3.5.2              
[43] Seurat_5.3.0                SeuratObject_5.1.0          sp_2.2-0                   
[46] runSeurat3_0.1.0            ExploreSCdataSeurat3_0.1.0 

loaded via a namespace (and not attached):
  [1] igraph_2.1.4              ica_1.0-3                 plotly_4.10.4            
  [4] Formula_1.2-5             zlibbioc_1.50.0           tidyselect_1.2.1         
  [7] bit_4.6.0                 doParallel_1.0.17         clue_0.3-66              
 [10] lattice_0.22-7            rjson_0.2.23              blob_1.2.4               
 [13] S4Arrays_1.4.1            pbkrtest_0.5.4            parallel_4.4.0           
 [16] png_0.1-8                 plotrix_3.8-4             ggplotify_0.1.2          
 [19] cli_3.6.5                 goftest_1.2-3             VIM_6.2.2                
 [22] variancePartition_1.34.0  BiocNeighbors_1.22.0      shadowtext_0.1.4         
 [25] uwot_0.2.3                curl_6.2.3                tidytree_0.4.6           
 [28] mime_0.13                 evaluate_1.0.3            ComplexHeatmap_2.20.0    
 [31] stringi_1.8.7             backports_1.5.0           lmerTest_3.1-3           
 [34] qqconf_1.3.2              httpuv_1.6.16             magrittr_2.0.3           
 [37] rappdirs_0.3.3            splines_4.4.0             ggraph_2.2.1             
 [40] sctransform_0.4.2         ggbeeswarm_0.7.2          DBI_1.2.3                
 [43] jquerylib_0.1.4           smoother_1.3              withr_3.0.2              
 [46] git2r_0.36.2              corpcor_1.6.10            reformulas_0.4.1         
 [49] class_7.3-23              rprojroot_2.0.4           lmtest_0.9-40            
 [52] tidygraph_1.3.1           colourpicker_1.3.0        htmlwidgets_1.6.4        
 [55] fs_1.6.6                  ggrepel_0.9.6             labeling_0.4.3           
 [58] fANCOVA_0.6-1             SparseArray_1.4.8         DESeq2_1.44.0            
 [61] ranger_0.17.0             DEoptimR_1.1-3-1          reticulate_1.42.0        
 [64] hexbin_1.28.5             zoo_1.8-14                XVector_0.44.0           
 [67] knitr_1.50                ggplot.multistats_1.0.1   UCSC.utils_1.0.0         
 [70] RhpcBLASctl_0.23-42       timechange_0.3.0          foreach_1.5.2            
 [73] patchwork_1.3.0           caTools_1.18.3            ggtree_3.12.0            
 [76] data.table_1.17.4         R.oo_1.27.1               RSpectra_0.16-2          
 [79] irlba_2.3.5.1             gridGraphics_0.5-1        fastDummies_1.7.5        
 [82] lazyeval_0.2.2            yaml_2.3.10               survival_3.8-3           
 [85] scattermore_1.2           crayon_1.5.3              RcppAnnoy_0.0.22         
 [88] RColorBrewer_1.1-3        progressr_0.15.1          tweenr_2.0.3             
 [91] later_1.4.2               ggridges_0.5.6            codetools_0.2-20         
 [94] GlobalOptions_0.1.2       aod_1.3.3                 KEGGREST_1.44.1          
 [97] Rtsne_0.17                shape_1.4.6.1             limma_3.60.6             
[100] pkgconfig_2.0.3           TMB_1.9.17                spatstat.univar_3.1-3    
[103] mathjaxr_1.8-0            EnvStats_3.1.0            aplot_0.2.5              
[106] scatterplot3d_0.3-44      ape_5.8-1                 spatstat.sparse_3.1-0    
[109] xtable_1.8-4              car_3.1-3                 plyr_1.8.9               
[112] httr_1.4.7                rbibutils_2.3             tools_4.4.0              
[115] globals_0.18.0            beeswarm_0.4.0            broom_1.0.8              
[118] nlme_3.1-168              assertthat_0.2.1          lme4_1.1-37              
[121] digest_0.6.37             numDeriv_2016.8-1.1       Matrix_1.7-3             
[124] farver_2.1.2              tzdb_0.5.0                remaCor_0.0.18           
[127] reshape2_1.4.4            yulab.utils_0.2.0         glue_1.8.0               
[130] cachem_1.1.0              polyclip_1.10-7           generics_0.1.4           
[133] Biostrings_2.72.1         mvtnorm_1.3-3             presto_1.0.0             
[136] parallelly_1.45.0         mnormt_2.1.1              statmod_1.5.0            
[139] RcppHNSW_0.6.0            ScaledMatrix_1.12.0       carData_3.0-5            
[142] minqa_1.2.8               pbapply_1.7-2             httr2_1.1.2              
[145] spam_2.11-1               gson_0.1.0                graphlayouts_1.2.2       
[148] gtools_3.9.5              ggsignif_0.6.4            RcppEigen_0.3.4.0.2      
[151] shiny_1.10.0              GenomeInfoDbData_1.2.12   glmmTMB_1.1.11           
[154] R.utils_2.13.0            memoise_2.0.1             rmarkdown_2.29           
[157] scales_1.4.0              R.methodsS3_1.8.2         RANN_2.6.2               
[160] Cairo_1.6-2               spatstat.data_3.1-6       rstudioapi_0.17.1        
[163] cluster_2.1.8.1           whisker_0.4.1             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] 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

integrate data from adult mLN and iLN

A.DeMartin

2025-05-06

preprocessing

load packages

load object all

subset adult

plot umaps

clustering

location

calculate cluster marker genes pre filter

features pre filter

filter object

rerun seurat after filter

load object adult

set color vectors

plot umaps

clustering

location

plot features

integrate data across location

load integrated object adult

assign label

Dimplot int data

clustering

label

label mLN iLN sep

mLN

iLN

label split by location

location

plot features int

cluster characterization

heatmap function

heatmap

session info