適用于scRNA-seq的細(xì)胞通訊工具-2：CellChat

現(xiàn)在開(kāi)始學(xué)習(xí)第二個(gè)適用于單細(xì)胞轉(zhuǎn)錄組數(shù)據(jù)的細(xì)胞通訊工具:Cellchat,也是比較常用的。相較于CellphoneDB的數(shù)據(jù)庫(kù)(配體-受體+多聚體),CellChat在模擬細(xì)胞間通訊的數(shù)據(jù)庫(kù)CellChatDB又納入輔因子，看了它的數(shù)據(jù)庫(kù)，相較于其他工具，它涵蓋的有關(guān)鼠的配、受體等會(huì)更多一些（可能對(duì)于小鼠的數(shù)據(jù)會(huì)更有優(yōu)勢(shì)一點(diǎn)）。另外，CellChat分析也可以用于多組間的比較，例如正常與疾病條件下的比較。結(jié)果的可視化也是很豐富。下面開(kāi)始學(xué)習(xí)一下吧！
Inference and analysis of cell-cell communication using CellChat - PubMed (nih.gov)

一、安裝


install.packages('NMF')
devtools::install_github("jokergoo/circlize")
devtools::install_github("jokergoo/ComplexHeatmap")
devtools::install_github("sqjin/CellChat")

二、加載R包、數(shù)據(jù)

library(Seurat)
library(SeuratData)
library(tidyverse)
library(CellChat)
library(NMF)
library(ggalluvial)
library(patchwork)
library(ggplot2)
library(svglite)
options(stringsAsFactors = FALSE)

pbmc <- readRDS("./pbmc.rds")

三、創(chuàng)建CellChat對(duì)象

cellchat <- createCellChat(pbmc@assays$RNA@data, meta = pbmc@meta.data, group.by = "cell_type")
summary(cellchat)
str(cellchat)
levels(cellchat@idents)
# [1] "Naive CD4 T"  "Memory CD4 T" "CD14+ Mono"  "B"            "CD8 T"     
# [6] "FCGR3A+ Mono" "NK"          "DC"          "Platelet"

groupSize <- as.numeric(table(cellchat@idents))  #查看每個(gè)cluster有多少個(gè)細(xì)胞
groupSize
[1] 684 481 476 344 291 162 155  32  13

四、導(dǎo)入配體受體數(shù)據(jù)庫(kù)

CellChatDB <- CellChatDB.human #小鼠是CellChatDB.mouse
str(CellChatDB) #查看數(shù)據(jù)庫(kù)信息，包含interaction、complex、cofactor和geneInfo
colnames(CellChatDB$interaction)
CellChatDB$interaction[1:4,1:4]
head(CellChatDB$cofactor)
head(CellChatDB$complex)
head(CellChatDB$geneInfo)
showDatabaseCategory(CellChatDB)
image1.png

#包括61.8%的旁分泌/自分泌信號(hào)相互作用、21.7%的細(xì)胞外基質(zhì)（ECM）-受體相互作用和16.5%的細(xì)胞-細(xì)胞接觸相互作用。48%的相互作用涉及雜聚分子復(fù)合物，52%的相互作用從最近的文獻(xiàn)中搜集的。


unique(CellChatDB$interaction$annotation)#查看可以選擇的側(cè)面，也就是上圖左中的三種

#這里選擇"Secreted Signaling"進(jìn)行后續(xù)細(xì)胞互作分析
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling") #也可默認(rèn)使用所有的
cellchat@DB <- CellChatDB.use # set the used database in the object

五、預(yù)處理

cellchat <- subsetData(cellchat)
future::plan("multicore", workers = 4)#multiprocess
cellchat <- identifyOverExpressedGenes(cellchat)#相當(dāng)于Seurat的FindMarkers，找每個(gè)細(xì)胞群中高表達(dá)的配受體基因
cellchat <- identifyOverExpressedInteractions(cellchat) #基于CellChatDB數(shù)據(jù)庫(kù)，識(shí)別出過(guò)表達(dá)的配體-受體相互作對(duì)。
cellchat <- projectData(cellchat, PPI.human) #找到配體受體關(guān)系后，projectData將配體受體對(duì)的表達(dá)值投射到PPI上，來(lái)對(duì)@data.signaling中的表達(dá)值進(jìn)行校正。結(jié)果保存在@data.project

六、推斷細(xì)胞通訊網(wǎng)路

#1.基于配體-受體水平推斷細(xì)胞通訊網(wǎng)絡(luò)
cellchat <- computeCommunProb(cellchat, raw.use = FALSE, population.size = TRUE) #如果不想用上一步PPI矯正的結(jié)果，raw.use = TRUE即可。#根據(jù)表達(dá)值推測(cè)細(xì)胞互作的概率,此步時(shí)間稍久一點(diǎn)兒
cellchat <- filterCommunication(cellchat, min.cells = 10)# Filter out the cell-cell communication if there are only few number of cells in certain cell groups
df.net <- subsetCommunication(cellchat)
write.csv(df.net, "net_lr.csv")

#2.基于信號(hào)通路水平推斷細(xì)胞通訊網(wǎng)絡(luò)
cellchat <- computeCommunProbPathway(cellchat)
df.netp <- subsetCommunication(cellchat, slot.name = "netP")
write.csv(df.netp, "net_pathway.csv")

七、細(xì)胞間通訊的推斷結(jié)果可視化階段：

cellchat <- aggregateNet(cellchat)#統(tǒng)計(jì)細(xì)胞間通信的數(shù)量（配受體對(duì)的數(shù)目）和強(qiáng)度（概率）
groupSize <- as.numeric(table(cellchat@idents))#計(jì)算每群細(xì)胞各有多少個(gè)

1.先總體看一下細(xì)胞間通訊的數(shù)量與強(qiáng)度

netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T,
                    label.edge= F, title.name = "Number of interactions")
netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, weight.scale = T,
                    label.edge= F, title.name = "Interaction weights/strength")
image2.png

2.下面展示分別以每種細(xì)胞類(lèi)型作為配體信號(hào)時(shí)的通訊網(wǎng)絡(luò)

細(xì)胞間通訊數(shù)目的推斷結(jié)果:
mat <- cellchat@net$count
par(mfrow = c(2,2),xpd = TRUE)
for (i in 1:nrow(mat)) {
  mat1<- matrix(0,nrow = nrow(mat),ncol = ncol(mat),dimnames = dimnames(mat))
  mat1[i, ] <- mat[i, ]
  netVisual_circle(mat1,vertex.weight = groupSize,
                  weight.scale = T,
                  arrow.width = 2,
                  arrow.size = 0.6,
                  edge.weight.max = max(mat),
                  title.name = rownames(mat)[i])}
image3_count.png
image4_count.png
細(xì)胞間通訊強(qiáng)度的推斷結(jié)果:
mat <- cellchat@net$weight
par(mfrow = c(2,2),xpd = T)
for (i in 1:nrow(mat)){
  mat2 <- matrix(0,nrow(mat),ncol = ncol(mat),dimnames =dimnames(mat))
  mat2[i, ] <- mat[i, ]
  netVisual_circle(mat2,
                  vertex.weight = groupSize,
                  weight.scale = T,
                  arrow.width = 2,
                  arrow.size = 0.6,
                  edge.weight.max = max(mat),
                  title.name = rownames(mat[i]))}
image5_weight.png
image6_weight.png
cellchat@netP$pathways  #查看信號(hào)通路
[1] "MIF"        "CCL"        "GALECTIN"  "IL2"        "ANNEXIN"    "LT"        "BAFF"     
[8] "FLT3"      "BTLA"      "TRAIL"      "TGFb"      "LIGHT"      "IL10"      "CSF"     
[15] "IL1"        "CD40"      "VISFATIN"  "GRN"        "IL16"      "IFN-II"    "BAG"     
[22] "PARs"      "FASLG"      "IL6"        "GAS"        "CXCL"      "COMPLEMENT" "NRG"     
[29] "PDGF"
pathways.show <- c("TGFb")  #選擇感興趣的通路，進(jìn)行后續(xù)的分析

3.某個(gè)信號(hào)通路或配-受體對(duì)信號(hào),介導(dǎo)的細(xì)胞間互作通訊的可視化

#下面依次展示層次圖、網(wǎng)絡(luò)圖、和弦圖、熱圖，這里雖然圖不同但是獲取的信息是一樣的。
levels(cellchat@idents)    # show all celltype
[1] "Naive CD4 T"  "Memory CD4 T" "CD14+ Mono"  "B"            "CD8 T"        "FCGR3A+ Mono"
[7] "NK"          "DC"          "Platelet"

vertex.receiver = c(1,3,5,7)
netVisual_aggregate(cellchat, signaling = pathways.show,  vertex.receiver = vertex.receiver,layout = 'hierarchy')

par(mfrow=c(1,1))
netVisual_aggregate(cellchat, signaling = pathways.show, layout = "circle")

par(mfrow=c(1,1))
netVisual_aggregate(cellchat, signaling = pathways.show, layout = "chord")

par(mfrow=c(1,1))
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds")
image_hierarchy.png
image_circle.png
image_chord.png
image_heatmap.png

4.在某個(gè)信號(hào)中，其包含的所有配-受體對(duì)，對(duì)該信號(hào)通路影響的大小(貢獻(xiàn)值)，依次可以展示層次圖、網(wǎng)絡(luò)圖、和弦圖

pathways.show = 'CD40'#這里隨意選定了'CD40'
netAnalysis_contribution(cellchat, signaling = pathways.show)
pairLR.CD40 <- extractEnrichedLR(cellchat, signaling = pathways.show, geneLR.return = FALSE) #提取對(duì)CD40有影響的所有配-受體
LR.show <- pairLR.CD40[1,] #提取對(duì)該通路影響最大的配-受體對(duì)

vertex.receiver = c(1,3,5,7)
netVisual_individual(cellchat, signaling = pathways.show,  pairLR.use = LR.show, vertex.receiver = vertex.receiver, layout = "hierarchy")
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "circle")
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "chord")
image_hierarchy.png
image_circle.png
image_chord.png

5.多個(gè)信號(hào)通路或配-受體對(duì),介導(dǎo)的細(xì)胞間互作通訊的可視化

levels(cellchat@idents)
[1] "Naive CD4 T"  "Memory CD4 T" "CD14+ Mono"  "B"            "CD8 T"        "FCGR3A+ Mono"
[7] "NK"          "DC"          "Platelet"

#指定source細(xì)胞和target細(xì)胞,展示氣泡圖
p = netVisual_bubble(cellchat, sources.use = c(2,4,6,8),
                    targets.use = c(3,5,7,9), remove.isolate = FALSE)
ggsave("bubble_1.pdf", p, width = 8, height = 12)
image_bubble_1.png
#同時(shí)指定"CCL"和"TGFb"這兩個(gè)信號(hào)通路
p1 = netVisual_bubble(cellchat, sources.use = c(2,4,6,8), targets.use = c(3,5,7,9),
                signaling = c("CCL","TGFb"), remove.isolate = FALSE)
ggsave("bubble_2.pdf", p1, width = 6, height = 8)
image_bubble_2.png
#挑選出細(xì)胞間互作顯著的配-受體對(duì)
pairLR.use <- extractEnrichedLR(cellchat, signaling = c("CCL","GALECTIN"))
netVisual_bubble(cellchat, sources.use = c(2,4,6,8), targets.use = c(3,5,7,9),
                pairLR.use = pairLR.use, remove.isolate = TRUE)
image_bubble_3.png

6.查看某個(gè)信號(hào)通路中，其中的配-受體基因在細(xì)胞群中的表達(dá)情況

plotGeneExpression(cellchat, signaling = "CCL")#小提琴圖

plotGeneExpression(cellchat, signaling = "CCL", type = "dot",color= "Reds")#點(diǎn)圖
image_violin.png
image_dot.png

對(duì)于單個(gè)樣本的cellchat方法的學(xué)習(xí)就先到這里吧，后面會(huì)再整理下多個(gè)樣本（多種疾病狀態(tài)）之間比較的分析處理。若有錯(cuò)誤之處，感謝指正！??
GitHub - sqjin/CellChat: R toolkit for inference, visualization and analysis of cell-cell communication from single-cell data

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