基于cellphonedb的单细胞互作弦图+受配体表达量【一键函数】

KS科研分享与服务-TS的美梦

发布于 2026-04-02 13:09:29

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前一期发布了关于cellchat结果的可视化复线（circlize系列（六）：复现Science单细胞互作弦图添加受配体表达量（基于cellchat分析结果）），马上有VIP群的小伙伴希望出cellphonedb的同款可视化，其实不催，我们也会出，熟悉我们内容的都知道，cellchat和cellphonedb是使用比较广泛的两种单细胞通讯工具，所以在很多的可视化上面两者基本是绑定出现的，“有cellchat一副美图的，绝对少不了cellphonedb”。

当然了，为了使用简洁方便，画图过程还是包装为一个函数。本来想直接套用cellchat模式，发现还是有些许不同。看看函数参数：

函数是关于cellphonedb V5分析结果的可视化，使用弦图展示celltype之间的互作，并展示具体的受配体，以及受配体的表达量。使用cellphonedb输出的pval和mean两个文件，以及相对应的seurat object进行可视化。同时还需要一个KS生信科研课手动整理的文件，关于cellphonedb受配体形式的转化。

读入数据：

setwd("/Users/ks_ts/Documents/公众号文章/pycirclize/circle单细胞互作基于cellphonedb分析结果")
GO_pvals <- read.delim("./示例数据/statistical_analysis_pvalues_12_21_2023_170246.txt", check.names = FALSE)
GO_means <- read.delim("./示例数据/statistical_analysis_means_12_21_2023_170246.txt", check.names = FALSE)
cpdb_anno <- read.csv('./cpdb_anno.csv', header = T, row.names = 1)
#加载单细胞object
load("~/Downloads/scRNA_Y16.Rdata")

自定义顺序颜色：

ks_cpdb_LRplot(file_pvals = GO_pvals,
               file_means = GO_means,
               cpdb_anno = cpdb_anno, 
               seurat_obj=scRNA_Y16,
               group.by = 'celltype',
               assay = 'RNA',
               source_cells = c("MC","Astro","LAMA","OPC","ODC","PN_RO","LAMPs"),
               target_cells = c("MC","Astro","LAMA","OPC","ODC","PN_RO","LAMPs"),
               comm_cut = 2,
               thresh=0.01,
               exp_shpe ='heatmap',
               celltype_order =  c("MC","Astro","LAMA","OPC","ODC","PN_RO","LAMPs"),
               group_colors = c("#8DD3C7","#FFFFB3","#BEBADA","#FB8072","#80B1D3","#FDB462","#B3DE69"))

换一种形式：

ks_cpdb_LRplot(file_pvals = GO_pvals,
               file_means = GO_means,
               cpdb_anno = cpdb_anno, 
               seurat_obj=scRNA_Y16,
               group.by = 'celltype',
               assay = 'RNA',
               source_cells = c("MC","Astro","LAMA","OPC","ODC","PN_RO","LAMPs"),
               target_cells = c("MC","Astro","LAMA","OPC","ODC","PN_RO","LAMPs"),
               comm_cut = 2,
               thresh=0.01,
               exp_shpe ='barplot',
               bg.border='black',
               celltype_order =  c("MC","Astro","LAMA","OPC","ODC","PN_RO","LAMPs"),
               group_colors = c("#8DD3C7","#FFFFB3","#BEBADA","#FB8072","#80B1D3","#FDB462","#B3DE69"))