Definition of CCL19 FRC subsets and their interactions with immune cells in NSCLC
Chrysa Papadopoulou
Last updated: 2025-10-01
Checks: 7 0
Knit directory: CCL19_FRCs_lung_cancer/
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Load packages
suppressPackageStartupMessages({
library(here)
library(purrr)
library(Seurat)
library(dittoSeq)
library(NMF)
library(CellChat)
library(harmony)
library(ggsci)
library(bigmds)
})Set directory
basedir <- here()Read NSCLC CCL19 FRC data
NSCLC_CCL19_data <- readRDS(paste0(basedir,"/data/Human/NSCLC_CCL19_FRCs_CAFs.rds"))NSCLC CCL19⁺ cells in NSCLC
#Define color palet
palet_CCL19_FRC <- c("#1B9E77", "#54B0E4","#E3BE00", "#E41A1C")
names(palet_CCL19_FRC) <- c("CAF2/TRC","CAF1/PRC","AdvFB" ,"SMC/PC")
palet_CCL19_FRC <- palet_CCL19_FRC[names(palet_CCL19_FRC) %in% unique(NSCLC_CCL19_data$cell_type)]
DimPlot(NSCLC_CCL19_data, reduction = "umap", group.by = "cell_type",cols = palet_CCL19_FRC)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle(paste0("CCL19", "\U207A ", "fibroblasts"))
Subset on CCL19⁺ FRCs
NCLS_FRCS <- subset(NSCLC_CCL19_data, cell_type %in% c("CAF2/TRC","CAF1/PRC"))
#Preprocessing
resolution <- c(0.1, 0.25, 0.4, 0.6,0.7, 0.8, 0.9, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)
NCLS_FRCS <- FindVariableFeatures(NCLS_FRCS, selection.method = "vst", nfeatures = 2000)
NCLS_FRCS <- ScaleData(NCLS_FRCS)
NCLS_FRCS <- RunPCA(object = NCLS_FRCS, npcs = 30, verbose = FALSE,seed.use = 8734)
NCLS_FRCS <- RunTSNE(object = NCLS_FRCS, reduction = "pca", dims = 1:20, seed.use = 8734)
NCLS_FRCS <- RunUMAP(object = NCLS_FRCS, reduction = "pca", dims = 1:20, seed.use = 8734)
NCLS_FRCS <- FindNeighbors(object = NCLS_FRCS, reduction = "pca", dims = 1:20, seed.use = 8734)
for(k in 1:length(resolution)){
NCLS_FRCS <- FindClusters(object = NCLS_FRCS, resolution = resolution[k], random.seed = 8734)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9474
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: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9089
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: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8838
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8560
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: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8432
Number of communities: 13
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8315
Number of communities: 15
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8227
Number of communities: 16
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8139
Number of communities: 17
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7990
Number of communities: 17
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7839
Number of communities: 18
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7705
Number of communities: 21
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7591
Number of communities: 22
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5023
Number of edges: 172182
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7483
Number of communities: 23
Elapsed time: 0 secondsUmap of Re-embedded CCL19⁺ FRC subsets (Figure 4B)
palet_CCL19_FRC <- c("#5050FFFF", "#CE3D32FF")
names(palet_CCL19_FRC) <- c("CAF2/TRC","CAF1/PRC")
DimPlot(NCLS_FRCS, reduction = "umap", group.by = "cell_type",cols = palet_CCL19_FRC)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle(paste0("CCL19", "\U207A ", "Tumor FRCs"))
Tonsillar FRC signatures projected on tumor CCL19⁺ FRC subsets (Figure 4C)
Signature SLO-TRC
NSCLC_TRC <-c("CCL19","CCL21","PDPN","ICAM1","VCAM1","LUM","PDGFRA","TNFSF13B")
NSCLC_TRC <- unlist(lapply(NSCLC_TRC, function(x) {
get_full_gene_name(x,NCLS_FRCS)
}))
slot_type <-"data"
gn <- "Tumor_TRC"
Visualize_GeneSignatures_sc(NCLS_FRCS, NSCLC_TRC, slot_type, 'average.mean',gn) + ggtitle("SLO-TRC signature")[1] "gene.set.score_Tumor_TRC_data"
Cell names order match in 'mean_expr' and 'object@meta.data':
adding gene set mean expression values in 'object@meta.data$gene.set.score'
Signature SLO-PRC
NSCLC_PRC <-c("CCL19","CCL21","ITGA1","ITGA7","MCAM","CNN1","NOTCH3","ACTA2","PDGFRB","ANGPT2")
NSCLC_PRC <- unlist(lapply(NSCLC_PRC, function(x) {
get_full_gene_name(x,NCLS_FRCS)
}))
slot_type <-"data"
gn <- "Tumor_PRC"
Visualize_GeneSignatures_sc(NCLS_FRCS, NSCLC_PRC, slot_type, 'average.mean',gn) + ggtitle("SLO-PRC signature")[1] "gene.set.score_Tumor_PRC_data"
Cell names order match in 'mean_expr' and 'object@meta.data':
adding gene set mean expression values in 'object@meta.data$gene.set.score'
Comparison of transcriptional profiles between NSCLC CCL19⁺ FRCs and Tonsilar FRCs via Multidimensional scaling (MDS)
Read Tonsilar FRC data
Tons_FRC_data <-readRDS(paste0(basedir,"/data/Human/mergedHumanTonsilExtendedDataset_incAcuteTonsilitis_mapped_wocl11+12+14_seuratFRC.rds"))Define color palette
cols<- pal_igv()(51)
names(cols) <- c(0:50)Tonsilar FRCs
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_1"] <- paste0("ACTA2", expression("\U207A"),"PRC_1")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_2"] <- paste0("ACTA2", expression("\U207A"),"PRC_2")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_3"] <- paste0("ACTA2", expression("\U207A"),"PRC_3")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_4"] <- paste0("ACTA2", expression("\U207A"),"PRC_4")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_5"] <- paste0("ACTA2", expression("\U207A"),"PRC_5")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "FDC_6"] <- "FDC"
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "PI16+RC_10"] <- paste0("PI16", expression("\U207A"),"RC_1")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "PI16+RC_11"] <- paste0("PI16", expression("\U207A"),"RC_2")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "PI16+RC_12"] <- paste0("PI16", expression("\U207A"),"RC_3")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "TRC_7"] <- "TRC_1"
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "TRC_8"] <- "TRC_2"
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "TRC_9"] <- "TRC_3"
colDataset <- cols[3:15]
names(colDataset) <- unique(Tons_FRC_data$clusterLabel)
DimPlot(Tons_FRC_data, reduction = "umap", group.by = "clusterLabel",cols=colDataset)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle("Tonsilar FRCs (De Martin et al 2023)")
Merge NSCLC TRC and PRC with Tonsilar FRCs
NCLS_FRCS$Disease_short <-rep("NSCLC",nrow(NCLS_FRCS@meta.data))
Tons_FRC_data$Disease_short <-rep("Tonsil",nrow(Tons_FRC_data@meta.data))
colnames(Tons_FRC_data@meta.data)[names(Tons_FRC_data@meta.data) == 'clusterLabel'] <- 'cell_type'
data_merge <- merge(NCLS_FRCS, y = c(Tons_FRC_data),
add.cell.ids = c("NCLS_FRCS","Tons_FRC_data"),
project = "merge_nsclc_tonsils")
#Preprocessing
resolution <- c(0.1, 0.25, 0.4, 0.6,0.8, 1.)
data_merge <- preprocessing(data_merge,resolution)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9641
Number of communities: 6
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9342
Number of communities: 12
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9153
Number of communities: 13
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8977
Number of communities: 17
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8821
Number of communities: 21
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8698
Number of communities: 27
Elapsed time: 5 secondsIntegrate data to correct for batch effects due to different tissues via seurat
Step 1
obj.list <-SplitObject(data_merge, split.by = 'cell_type')
#For each object in list we see to run normalization and identify highly variable features
for (i in 1:length(obj.list)){
#Normalization
obj.list[[i]] <- NormalizeData(obj.list[[i]], normalization.method = "LogNormalize", scale.factor = 10000)
#Find high variable genes
obj.list[[i]] <- FindVariableFeatures(obj.list[[i]], selection.method = "vst", nfeatures = 2000)
}Step 2
#select features that are repeatedly variable across datasets for integration
features <- SelectIntegrationFeatures(object.list = obj.list)
#Find anchors to integrate the data across different patients (Canonical correlation analysis)
anchors <- FindIntegrationAnchors(object.list = obj.list, anchor.features = features)
# Create an 'integrated' data assay
seurat_integrated <- IntegrateData(anchorset = anchors)Step 3
# We run a single integrated analysis on all cells!
DefaultAssay(seurat_integrated) <- "integrated"
# Run the standard workflow for visualization and clustering
seurat_integrated <- ScaleData(seurat_integrated, verbose = FALSE)
seurat_integrated <- RunPCA(object = seurat_integrated, npcs = 30, verbose = FALSE,seed.use = 8734)
seurat_integrated <- RunTSNE(object = seurat_integrated, reduction = "pca", dims = 1:20, seed.use = 8734)
seurat_integrated<- RunUMAP(object = seurat_integrated, reduction = "pca", dims = 1:20, seed.use = 8734)
seurat_integrated <- FindNeighbors(object = seurat_integrated, reduction = "pca", dims = 1:20, seed.use = 8734)
#Clustering
resolution <- c(0.1, 0.25, 0.4, 0.6,0.8, 1.,1.2,1.4,1.8)
for(k in 1:length(resolution)){
seurat_integrated <- FindClusters(object = seurat_integrated, resolution = resolution[k], random.seed = 8734)
}Apply Divide and conquer MDS algorithm proposed by Delicado P. and C. Pachón-García (2021) for fast MDS computation due to large dataset size
# celltype similarity wth MDS
DefaultAssay(seurat_integrated) <-'integrated'
#Divide-andconquer MDS proposed by Delicado P. and C. Pachón-García (2021)
#MDS computation
mds <- divide_conquer_mds(x = t(GetAssayData(seurat_integrated, slot = 'scale.data')), l = 200, c_points = 5 * 2, r = 2, n_cores = 1)$points
colnames(mds) <- paste0("MDSDIVCONQ_", 1:2)
# Store MDS representation as a custom dimensional reduction field
seurat_integrated[['mds_div_conq']] <- CreateDimReducObject(embeddings = mds, key = 'MDSDIVCONQ_', assay = DefaultAssay(seurat_integrated))
# Save seurat object for later use
#saveRDS(seurat_integrated,paste0(basedir,"/data/Human/Tonsil_Ccl19_TRC_PRC_final_mds_div_conq.rds"))Multidimensional scaling (MDS) plot
Read integrated object with MDS representation
seurat_integrated <- readRDS(paste0(basedir,"/data/Human/Tonsil_Ccl19_TRC_PRC_final_mds_conq.rds"))Visualize MDS plot (Figure 4D)
mds_tx_condition <- seurat_integrated@reductions$mds_div_conq@cell.embeddings %>%
as.data.frame() %>% cbind(tx = seurat_integrated@meta.data$Disease_short)
mds_tx_celltype <- seurat_integrated@reductions$mds_div_conq@cell.embeddings %>%
as.data.frame() %>% cbind(tx = seurat_integrated@meta.data$cell_type)
mds_tx_TOTAL <- merge(mds_tx_condition, mds_tx_celltype, by=c("MDSDIVCONQ_1", "MDSDIVCONQ_2"), all.x=T, all.y=T)
colnames(mds_tx_TOTAL) <-c("MDS_1", "MDS_2", "Condition","Celltype")
#Color palette
colDataset <- cols[1:15]
names(colDataset) <- unique(seurat_integrated$cell_type)
# Use mean gaussian kernel
mds_tx_TOTAL_gk <- mds_tx_TOTAL %>%
group_by(Celltype,Condition) %>%
mutate(count_mds1 = mean(GK(MDS_1))) %>%
mutate(count_mds2 = mean(GK(MDS_2)))
ggplot(mds_tx_TOTAL_gk, aes(x=count_mds1, y=count_mds2, color=Celltype, shape = Condition)) + geom_point(stroke = 1.5) + ylab("MDS2") + xlab("MDS1") + coord_cartesian(xlim = c(0, max(mds_tx_TOTAL_gk$count_mds1,mds_tx_TOTAL_gk$count_mds2)), ylim = c(0, max(mds_tx_TOTAL_gk$count_mds1,mds_tx_TOTAL_gk$count_mds2)) ) +
scale_color_manual(values=colDataset) + scale_shape_manual(values = c(2, 3)) +
theme(aspect.ratio = 2,axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.text.y = element_text(angle = 0, vjust = 0.5,colour = "black",size = 10),
axis.text.x = element_text(angle = 0, vjust = 0.5,colour = "black",size = 10)) 
Read NSCLC TIL data
NSCLS_TIL_data <- readRDS(paste0(basedir,"/data/Human/NSCLC_TILs.rds"))Merge NSCLS CCL19⁺ FRCs and NSCLS TILs
same_columns <- intersect(colnames(NSCLS_TIL_data@meta.data),colnames(NCLS_FRCS@meta.data))
NSCLS_TIL_data@meta.data <-NSCLS_TIL_data@meta.data[,same_columns]
NCLS_FRCS@meta.data <-NCLS_FRCS@meta.data[,same_columns]
merged_data<- merge(NSCLS_TIL_data, y = c(NCLS_FRCS),
add.cell.ids = c('NSCLS_TIL_data','NCLS_FRCS'),
project = "NSCLC_FRC_TIL")
resolution <- c(0.1, 0.25, 0.4, 0.6, 0.8, 1.,1.2,1.4,1.6,2.)
merged_data <- preprocessing(merged_data,resolution)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 11699
Number of edges: 402688
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9692
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 11699
Number of edges: 402688
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9416
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: 11699
Number of edges: 402688
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9236
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: 11699
Number of edges: 402688
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9030
Number of communities: 15
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 11699
Number of edges: 402688
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8869
Number of communities: 20
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 11699
Number of edges: 402688
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8738
Number of communities: 21
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 11699
Number of edges: 402688
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8610
Number of communities: 22
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 11699
Number of edges: 402688
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8499
Number of communities: 24
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 11699
Number of edges: 402688
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8399
Number of communities: 28
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 11699
Number of edges: 402688
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8217
Number of communities: 29
Elapsed time: 1 secondsFRC-immune cell interactions in NSCLC via Cellchat (Suoqin Jin et al., 2021)
Convert seurat object to cellchat object
#Define color palet
palet <- c("#5050FFFF", "#CE3D32FF", "#4DAF4A","#FB9A99","#377EB8","#A65628","#222F75")
names(palet) <- c( "CAF2/TRC","CAF1/PRC", paste0("CD4", "\u207A ", "T cells"), paste0("CD8", "\u207A ", "T cells"), "B cells", "Regulatory T cells",paste0("Cycling CD8", "\u207A ", "T cells"))
# Interactome analysis
cellchat <- Cellchat_Analysis(merged_data)[1] "Create a CellChat object from a data matrix"
Set cell identities for the new CellChat object
The cell groups used for CellChat analysis are B cells CAF1/PRC CAF2/TRC CD4⁺ T cells CD8⁺ T cells Cycling CD8⁺ T cells Regulatory T cells cellchat <-CellChatDownstreamAnalysis(cellchat,"human",thresh = 0.05)triMean is used for calculating the average gene expression per cell group.
[1] ">>> Run CellChat on sc/snRNA-seq data <<< [2025-10-01 17:47:36.645817]"
[1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2025-10-01 17:49:04.240146]"Interactome analysis (Figure 4E)
gg <- netAnalysis_signalingRole_scatter(cellchat,color.use = palet)
gg <- gg + ggtitle("Interactome analysis (Cellchat)")
gg
### Outgoing signaling patterns
selectK(cellchat, pattern = "outgoing")
Incoming signaling patterns
selectK(cellchat, pattern = "incoming")
Incoming and outgoing patterns
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "outgoing", k = 5, color.use = palet)
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "incoming", k = 6, color.use = palet)Joint dotplot for highlighting incoming and outcoming communication patterns explicitly for pathways with active signaling between FRCs and immune cells (Figure 4F)
order_list <-c("CAF2/TRC","CAF1/PRC",
paste0("CD8", "\u207A ", "T cells"),
paste0("Cycling CD8", "\u207A ", "T cells"),
paste0("CD4", "\u207A ", "T cells"),"B cells", "Regulatory T cells")
pathways<- cellchat@netP$pathways
gg <-comAnalysis_joint_dot(cellchat,color.use = palet,font.size = 8,pathways = pathways, order_list = order_list)$gg
shared_signaling <- comAnalysis_joint_dot(cellchat,color.use = palet,font.size = 8,pathways = pathways, order_list = order_list)$new_df_list
gg
Session info
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] doParallel_1.0.17 iterators_1.0.14 foreach_1.5.2
[4] bigmds_3.0.0 ggsci_3.0.0 harmony_1.2.0
[7] Rcpp_1.0.11 CellChat_1.6.1 igraph_1.5.0.1
[10] dplyr_1.1.2 NMF_0.26 Biobase_2.60.0
[13] BiocGenerics_0.46.0 cluster_2.1.4 rngtools_1.5.2
[16] registry_0.5-1 dittoSeq_1.12.1 ggplot2_3.4.2
[19] SeuratObject_5.1.0 Seurat_4.3.0.1 purrr_1.0.1
[22] here_1.0.1 magrittr_2.0.3 circlize_0.4.15
[25] tidyr_1.3.0 tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.1
[3] later_1.3.1 bitops_1.0-7
[5] polyclip_1.10-4 ggnetwork_0.5.12
[7] lifecycle_1.0.3 rstatix_0.7.2
[9] rprojroot_2.0.3 globals_0.16.2
[11] processx_3.8.2 lattice_0.21-8
[13] MASS_7.3-60 backports_1.4.1
[15] plotly_4.10.2 sass_0.4.7
[17] rmarkdown_2.23 jquerylib_0.1.4
[19] yaml_2.3.7 httpuv_1.6.11
[21] sctransform_0.4.1 spam_2.10-0
[23] sp_2.0-0 spatstat.sparse_3.0-2
[25] reticulate_1.36.1 cowplot_1.1.1
[27] pbapply_1.7-2 RColorBrewer_1.1-3
[29] abind_1.4-5 zlibbioc_1.46.0
[31] Rtsne_0.16 GenomicRanges_1.52.0
[33] RCurl_1.98-1.12 pracma_2.4.4
[35] git2r_0.33.0 GenomeInfoDbData_1.2.10
[37] IRanges_2.34.1 S4Vectors_0.38.1
[39] svd_0.5.5 ggrepel_0.9.3
[41] irlba_2.3.5.1 listenv_0.9.0
[43] spatstat.utils_3.1-0 pheatmap_1.0.12
[45] RSpectra_0.16-1 goftest_1.2-3
[47] spatstat.random_3.1-5 fitdistrplus_1.1-11
[49] parallelly_1.36.0 svglite_2.1.1
[51] leiden_0.4.3 codetools_0.2-19
[53] DelayedArray_0.28.0 tidyselect_1.2.0
[55] shape_1.4.6 farver_2.1.1
[57] matrixStats_1.0.0 stats4_4.3.1
[59] spatstat.explore_3.2-1 jsonlite_1.8.7
[61] GetoptLong_1.0.5 BiocNeighbors_1.18.0
[63] ellipsis_0.3.2 progressr_0.13.0
[65] ggalluvial_0.12.5 ggridges_0.5.4
[67] survival_3.5-5 systemfonts_1.0.4
[69] tools_4.3.1 ragg_1.2.5
[71] sna_2.7-1 ica_1.0-3
[73] glue_1.6.2 gridExtra_2.3
[75] SparseArray_1.2.4 xfun_0.39
[77] MatrixGenerics_1.12.3 GenomeInfoDb_1.36.1
[79] withr_2.5.0 BiocManager_1.30.21.1
[81] fastmap_1.1.1 fansi_1.0.4
[83] callr_3.7.3 digest_0.6.33
[85] R6_2.5.1 mime_0.12
[87] textshaping_0.3.6 colorspace_2.1-0
[89] Cairo_1.6-1 scattermore_1.2
[91] tensor_1.5 spatstat.data_3.0-1
[93] utf8_1.2.3 generics_0.1.3
[95] data.table_1.14.8 FNN_1.1.3.2
[97] httr_1.4.6 htmlwidgets_1.6.2
[99] S4Arrays_1.2.1 whisker_0.4.1
[101] uwot_0.1.16 pkgconfig_2.0.3
[103] gtable_0.3.3 ComplexHeatmap_2.16.0
[105] lmtest_0.9-40 SingleCellExperiment_1.22.0
[107] XVector_0.40.0 htmltools_0.5.5
[109] carData_3.0-5 dotCall64_1.1-1
[111] clue_0.3-64 scales_1.2.1
[113] png_0.1-8 knitr_1.43
[115] rstudioapi_0.15.0 rjson_0.2.21
[117] reshape2_1.4.4 coda_0.19-4
[119] statnet.common_4.9.0 nlme_3.1-162
[121] cachem_1.0.8 zoo_1.8-12
[123] GlobalOptions_0.1.2 stringr_1.5.0
[125] KernSmooth_2.23-22 miniUI_0.1.1.1
[127] pillar_1.9.0 grid_4.3.1
[129] vctrs_0.6.3 RANN_2.6.1
[131] ggpubr_0.6.0 promises_1.2.0.1
[133] car_3.1-2 xtable_1.8-4
[135] evaluate_0.21 cli_3.6.1
[137] compiler_4.3.1 rlang_1.1.1
[139] crayon_1.5.2 ggsignif_0.6.4
[141] future.apply_1.11.0 labeling_0.4.2
[143] ps_1.7.5 forcats_1.0.0
[145] getPass_0.2-4 plyr_1.8.8
[147] fs_1.6.3 stringi_1.7.12
[149] network_1.18.1 BiocParallel_1.34.2
[151] viridisLite_0.4.2 deldir_1.0-9
[153] gridBase_0.4-7 munsell_0.5.0
[155] lazyeval_0.2.2 spatstat.geom_3.2-4
[157] Matrix_1.6-4 patchwork_1.1.2
[159] future_1.33.0 shiny_1.7.4.1
[161] highr_0.10 SummarizedExperiment_1.30.2
[163] ROCR_1.0-11 broom_1.0.5
[165] bslib_0.5.0 date()[1] "Wed Oct 1 17:52:45 2025"
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] doParallel_1.0.17 iterators_1.0.14 foreach_1.5.2
[4] bigmds_3.0.0 ggsci_3.0.0 harmony_1.2.0
[7] Rcpp_1.0.11 CellChat_1.6.1 igraph_1.5.0.1
[10] dplyr_1.1.2 NMF_0.26 Biobase_2.60.0
[13] BiocGenerics_0.46.0 cluster_2.1.4 rngtools_1.5.2
[16] registry_0.5-1 dittoSeq_1.12.1 ggplot2_3.4.2
[19] SeuratObject_5.1.0 Seurat_4.3.0.1 purrr_1.0.1
[22] here_1.0.1 magrittr_2.0.3 circlize_0.4.15
[25] tidyr_1.3.0 tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.1
[3] later_1.3.1 bitops_1.0-7
[5] polyclip_1.10-4 ggnetwork_0.5.12
[7] lifecycle_1.0.3 rstatix_0.7.2
[9] rprojroot_2.0.3 globals_0.16.2
[11] processx_3.8.2 lattice_0.21-8
[13] MASS_7.3-60 backports_1.4.1
[15] plotly_4.10.2 sass_0.4.7
[17] rmarkdown_2.23 jquerylib_0.1.4
[19] yaml_2.3.7 httpuv_1.6.11
[21] sctransform_0.4.1 spam_2.10-0
[23] sp_2.0-0 spatstat.sparse_3.0-2
[25] reticulate_1.36.1 cowplot_1.1.1
[27] pbapply_1.7-2 RColorBrewer_1.1-3
[29] abind_1.4-5 zlibbioc_1.46.0
[31] Rtsne_0.16 GenomicRanges_1.52.0
[33] RCurl_1.98-1.12 pracma_2.4.4
[35] git2r_0.33.0 GenomeInfoDbData_1.2.10
[37] IRanges_2.34.1 S4Vectors_0.38.1
[39] svd_0.5.5 ggrepel_0.9.3
[41] irlba_2.3.5.1 listenv_0.9.0
[43] spatstat.utils_3.1-0 pheatmap_1.0.12
[45] RSpectra_0.16-1 goftest_1.2-3
[47] spatstat.random_3.1-5 fitdistrplus_1.1-11
[49] parallelly_1.36.0 svglite_2.1.1
[51] leiden_0.4.3 codetools_0.2-19
[53] DelayedArray_0.28.0 tidyselect_1.2.0
[55] shape_1.4.6 farver_2.1.1
[57] matrixStats_1.0.0 stats4_4.3.1
[59] spatstat.explore_3.2-1 jsonlite_1.8.7
[61] GetoptLong_1.0.5 BiocNeighbors_1.18.0
[63] ellipsis_0.3.2 progressr_0.13.0
[65] ggalluvial_0.12.5 ggridges_0.5.4
[67] survival_3.5-5 systemfonts_1.0.4
[69] tools_4.3.1 ragg_1.2.5
[71] sna_2.7-1 ica_1.0-3
[73] glue_1.6.2 gridExtra_2.3
[75] SparseArray_1.2.4 xfun_0.39
[77] MatrixGenerics_1.12.3 GenomeInfoDb_1.36.1
[79] withr_2.5.0 BiocManager_1.30.21.1
[81] fastmap_1.1.1 fansi_1.0.4
[83] callr_3.7.3 digest_0.6.33
[85] R6_2.5.1 mime_0.12
[87] textshaping_0.3.6 colorspace_2.1-0
[89] Cairo_1.6-1 scattermore_1.2
[91] tensor_1.5 spatstat.data_3.0-1
[93] utf8_1.2.3 generics_0.1.3
[95] data.table_1.14.8 FNN_1.1.3.2
[97] httr_1.4.6 htmlwidgets_1.6.2
[99] S4Arrays_1.2.1 whisker_0.4.1
[101] uwot_0.1.16 pkgconfig_2.0.3
[103] gtable_0.3.3 ComplexHeatmap_2.16.0
[105] lmtest_0.9-40 SingleCellExperiment_1.22.0
[107] XVector_0.40.0 htmltools_0.5.5
[109] carData_3.0-5 dotCall64_1.1-1
[111] clue_0.3-64 scales_1.2.1
[113] png_0.1-8 knitr_1.43
[115] rstudioapi_0.15.0 rjson_0.2.21
[117] reshape2_1.4.4 coda_0.19-4
[119] statnet.common_4.9.0 nlme_3.1-162
[121] cachem_1.0.8 zoo_1.8-12
[123] GlobalOptions_0.1.2 stringr_1.5.0
[125] KernSmooth_2.23-22 miniUI_0.1.1.1
[127] pillar_1.9.0 grid_4.3.1
[129] vctrs_0.6.3 RANN_2.6.1
[131] ggpubr_0.6.0 promises_1.2.0.1
[133] car_3.1-2 xtable_1.8-4
[135] evaluate_0.21 cli_3.6.1
[137] compiler_4.3.1 rlang_1.1.1
[139] crayon_1.5.2 ggsignif_0.6.4
[141] future.apply_1.11.0 labeling_0.4.2
[143] ps_1.7.5 forcats_1.0.0
[145] getPass_0.2-4 plyr_1.8.8
[147] fs_1.6.3 stringi_1.7.12
[149] network_1.18.1 BiocParallel_1.34.2
[151] viridisLite_0.4.2 deldir_1.0-9
[153] gridBase_0.4-7 munsell_0.5.0
[155] lazyeval_0.2.2 spatstat.geom_3.2-4
[157] Matrix_1.6-4 patchwork_1.1.2
[159] future_1.33.0 shiny_1.7.4.1
[161] highr_0.10 SummarizedExperiment_1.30.2
[163] ROCR_1.0-11 broom_1.0.5
[165] bslib_0.5.0