④单细胞学习-cellchat细胞间通讯

#预处理用于细胞通信分析的表达数据

cellchat <- subsetData(cellchat)

#future::plan("multiprocess", workers = 4) #可以不运行

cellchat <- identifyOverExpressedGenes(cellchat)#分析差异基因

cellchat <- identifyOverExpressedInteractions(cellchat)#

cellchat <- projectData(cellchat, )

#进行细胞通信网络的推断

cellchat <- computeCommunProb(cellchat, raw.use = TRUE)#计算通信概率并推断cellchat网络

cellchat <- filterCommunication(cellchat, = 10)#过滤

#信号通路级别推断细胞-细胞通信

cellchat <- computeCommunProbPathway(cellchat)

#计算整合的细胞通信网络

cellchat <- aggregateNet(cellchat)

#进行可视化

groupSize <- as.numeric(table(cellchat@idents))

par(mfrow = c(1,2), xpd=TRUE)

netVisual_circle(cellchat@net$count, = groupSize,

= T, = F, = "Number of interactions")

netVisual_circle(cellchat@net$weight, = groupSize,

= T, = F, = "Interaction weights/strength")

mat <- cellchat@net$weight

par(mfrow = c(3,4), xpd=TRUE)##如果可视化出现问题可以把前面的图片保存清空后再作图

for (i in 1:nrow(mat)) {

mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))

mat2[i, ] <- mat[i, ]

netVisual_circle(mat2, = groupSize, = T, = max(mat), = rownames(mat)[i])

}

dev.off()

#细胞通信网络的可视化######################################

<- c("CXCL")

= seq(1,4) # a numeric vector.

netVisual_aggregate(cellchat, signaling = ,

= )

# Circle plot

par(mfrow=c(1,1))

netVisual_aggregate(cellchat, signaling = ,

layout = "circle")

# 弦图

par(mfrow=c(1,1))

netVisual_aggregate(cellchat, signaling = , layout = "chord")

#热图

par(mfrow=c(1,1))

netVisual_heatmap(cellchat, signaling = , = "Reds")

# Chord diagram

group.cellType <- c(rep("FIB", 4), rep("DC", 4), rep("TC", 4)) # grouping cell clusters into fibroblast, DC and TC cells

names(group.cellType) <- levels(cellchat@idents)

netVisual_chord_cell(cellchat, signaling = ,group = group.cellType,

= paste0(, " signaling network"))

#计算每个配体受体对整体信号通路的贡献，并可视化由单个配体受体对调节的细胞通信

netAnalysis_contribution(cellchat, signaling = )

#视化由单个配体受体对调节的细胞-细胞通信

<- extractEnrichedLR(cellchat, signaling = , geneLR.return = FALSE)

<- [1,] # show one ligand-receptor pair

= seq(1,4) # # Hierarchy plot a numeric vector

netVisual_individual(cellchat, signaling = ,

pairLR.use = , = )

# Circle plot

netVisual_individual(cellchat, signaling = ,

pairLR.use = , layout = "circle")

#相关保存

.all <- cellchat@netP$pathways

# check the order of cell identity to set suitable

levels(cellchat@idents)

= seq(1,4)

for (i in 1:length(.all)) {

# Visualize communication network associated with both signaling pathway and individual L-R pairs

netVisual(cellchat, signaling = .all[i], = , layout = "hierarchy")

# Compute and visualize the contribution of each ligand-receptor pair to the overall signaling pathway

gg <- netAnalysis_contribution(cellchat, signaling = .all[i])

ggsave(filename=paste0(.all[i], "_L-R_contribution.pdf"), plot=gg, width = 3, height = 2, units = 'in', dpi = 300)

}

#可视化由多个配体受体或信号通路调节的细胞通信

netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:11),

= FALSE)

netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:11),

signaling = c("CCL","CXCL"), = FALSE)

netVisual_chord_gene(cellchat, sources.use = 4, targets.use = c(5:11),

= 0.5, = 30)

# show all the interactions received by

netVisual_chord_gene(cellchat, sources.use = c(1,2,3,4),

targets.use = 8, = 15)

#使用小提琴/点图绘制信号基因表达分布

plotGeneExpression(cellchat, signaling = "CXCL")

plotGeneExpression(cellchat, signaling = "CXCL",

= FALSE)#提取与推断的L-R对或信号通路相关的信号基因

#细胞通信网络系统分析############################

cellchat <- netAnalysis_computeCentrality(cellchat, = "netP") # the slot 'netP' means the inferred intercellular communication network of signaling pathways

netAnalysis_signalingRole_network(cellchat, signaling = ,

width = 8, height = 2.5, font.size = 10)

#使用散点图在 2D 空间中可视化占主导地位的发射器（源）和接收器（目标）

gg1 <- netAnalysis_signalingRole_scatter(cellchat)

gg2 <- netAnalysis_signalingRole_scatter(cellchat, signaling = c("CXCL", "CCL"))

gg1 + gg2

#哪些信号对某些细胞组的传出或传入信号贡献最大

ht1 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "outgoing")

ht2 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "incoming")

ht1 + ht2

ht <- netAnalysis_signalingRole_heatmap(cellchat, signaling = c("CXCL", "CCL"))

#识别和可视化分泌细胞的传出通信模式##############################

selectK(cellchat, pattern = "outgoing")#selectK推断模式的数量。

#当传出模式数为 3 时，Cophenetic 和Silhouette值都开始突然下降。

nPatterns = 3

cellchat <- identifyCommunicationPatterns(cellchat,

pattern = "outgoing", k = nPatterns)

netAnalysis_dot(cellchat, pattern = "outgoing")#绘制点图

selectK(cellchat, pattern = "incoming")#识别和可视化目标细胞的传入通信模式

nPatterns = 4

cellchat <- identifyCommunicationPatterns(cellchat, pattern = "incoming", k = nPatterns)

# river plot桑基图

library(ggalluvial)

netAnalysis_river(cellchat, pattern = "incoming")

# dot plot

netAnalysis_dot(cellchat, pattern = "outgoing")

#信号网络的流形和分类学习分析

cellchat <- computeNetSimilarity(cellchat, type = "functional")

cellchat <- netEmbedding(cellchat, type = "functional")

#> Manifold learning of the signaling networks for a single dataset

cellchat <- netClustering(cellchat, type = "functional")

#> Classification learning of the signaling networks for a single dataset

# Visualization in 2D-space

netVisual_embedding(cellchat, type = "functional", label.size = 3.5)

#最后保存

saveRDS(cellchat, file = "cellchat_humanSkin_LS.rds")