Cellchat细胞互作分析可视化：依然是解决问题

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针对cellchat更新的问题我们之前说过一期了：Cellchat和Cellphonedb细胞互作一些问题的解决(error和可视化）。但是小伙伴反应还是出问题，cellchat和igraph报的版本都对，主要是做贝壳图那里循环的时候出错。微信VIP群里尝试无果后，我们决定来一次头脑风暴，其实实质就是利用循环作图，可是怎么总是出错，思考一下改写一下。

顺便近期小伙伴问道cellchat部分的可视化，这里就补充上了。因为cellchat可视化内容实在太多，很多我没有兴趣，也不知道怎么解释应用到自己的分析，还是建议看看原文作者的解释，选适合自己的可视化。

详细分析间：Cellchat（代码详细注释版）：单细胞转录组（人、小鼠）细胞互作分析及可视化。这里仅仅是部分可视化。

一、贝壳图

#----------------------------------一、贝壳图-----------------------------------# #展示每个亚群作为source的信号传递# mat <- group1_cellchat@net$weight# par(mfrow = c(5,4), xpd=TRUE,mar=c(1,1,1,1))# 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, #                    vertex.weight = groupSize, #                    weight.scale = T, #                    edge.weight.max = max(mat), #                    title.name = rownames(mat)[i])# }# Error in i_set_edge_attr(x, attr(value, "name"), index = value, value = attr(value,  : # Length of new attribute value must be 1 or 7, the number of target edges, not 2
#多少种细胞就设置多少个颜色cell_color <- c("#20B2AA","#FFA500","#9370DB",                "#98FB98","#F08080","#1E90FF","#7CFC00")                         color_data <- data.frame(cell_color)for (i in 1:length(cell_color)){  color1 <- c(cell_color[i], setdiff(cell_color, cell_color[i]))  color1 <- as.data.frame(color1)  color_data <- cbind(color_data, color1)}
color_data <- color_data[, -1]

#展示每个亚群作为source的信号传递mat <- mycellchat@net$weightpar(mfrow = c(2,4), xpd=TRUE,mar=c(2,2,2,2))for (i in 1:nrow(mat)) {  mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))  mat2[i, ] <- mat[i, ]  mat2 <- as.data.frame(mat2)  data <- data.frame(mat2[i,])  data <- select(data,i,everything())    data1 <- data.frame(mat2[-i, ])  data1 <- select(data1,i,everything())    data2 <- rbind(data, data1)  data2 <- as.matrix(data2)    netVisual_circle(data2,                    vertex.weight = groupSize,                    weight.scale = T,                    edge.weight.max = max(mat),                    title.name = rownames(data2)[1],                   color.use = color_data[, i])}

二、可视化通路基因表达

#----------------------------------二、通路基因表达-----------------------------pathway.show <- unique(group1.net$pathway_name)#TGFbp1 <- plotGeneExpression(mycellchat, signaling = 'TGFb')# color.use = NULL,# group.by = NULL,

三、河流图

#----------------------------------三、河流图-------------------------------------library(NMF)library(ggalluvial)#选择合适的nPatternsmycellchat <- netAnalysis_computeCentrality(mycellchat)selectK(mycellchat, pattern = "outgoing")
#outgoingnPatterns = 4#选择第一次下降的拐点数字mycellchat <- identifyCommunicationPatterns(object = mycellchat,                                          pattern = "outgoing",                                          k = nPatterns)
netAnalysis_river(mycellchat, pattern = "outgoing",do.order=T)



#incomingselectK(mycellchat, pattern = "incoming")nPatterns = 4mycellchat <- identifyCommunicationPatterns(object = mycellchat,                                          pattern = "incoming",                                          k = nPatterns)netAnalysis_river(mycellchat, pattern = "incoming")

好了，这就是所有内容了。对了，小伙伴在解决第一个报错上有好的办法希望分享。觉得这个帖子对你有帮助的话，点个赞再走呗！