接上一节（【视频教程】Cellphonedb v5更新：python版Scanpy单细胞分析后续之cpdb细胞通讯）。我们通过对cpdb结果的整合得到cpdb_pbmc_summary，这里我们对结果进行可视化，主要是弦图的可视化，看了一下，之前好像对于cpdb没有做过太多的弦图。为了方便，我们借助了R包和一些文章，然后进行了修改，包装为可视化函数，方便使用。完整代码已发布微信VIP群，请自行下载！视频解说见B站：https://www.bilibili.com/video/BV1V4DVY8Ehq/?spm_id_from=333.999.0.0&vd_source=05b5479545ba945a8f5d7b2e7160ea34首先我们用整合结果做个常见的气泡图，就会很简单，可以筛选需要展示的互作，结果可以自行修饰。

setwd('D:\\KS项目\\公众号文章\\cpdb结果可视化之-气泡图-弦图')
df <- read.csv('./cpdb_pbmc_summary.txt', header = T, sep = '\t', row.names = 1)


# plot dotplot ------------------------------------------------------------
#提取需要的互作
# unique(df$celltype_pair)
df_sce <- df[df$celltype_pair %in% c("B|B","B|CD14 Monocytes","B|CD4 T","B|CD8 T","B|Dendritic",
                                   "B|FCGR3A Monocytes","B|NK","B|FCGR3A Monocytes"),]
#筛选显著的互作
df_sce <- df_sce[df_sce$pval <=0.05,]

colnames(df)
# [1] "interacting_pair" "celltype_pair"    "mean"             "pval" 

library(ggplot2)
#ggplot作图
ggplot(df_sce, aes(x=celltype_pair, y=interacting_pair, 
                      size= -log10(pval+0.001), color=mean)) + 
  geom_point(stat = "identity")+
  geom_point(aes(size= -log10(pval+0.001)), shape = 21, colour="black", stroke=1)+
  theme_bw()+
  scale_y_discrete(limits=rev)+
  scale_fill_viridis_c(option = "magma", guide = "colourbar", aesthetics = "color", direction=-1)+ 
  theme(axis.text.x = element_text(size = 10, angle=45, hjust=1, vjust=1, colour = 'black'),
        axis.text.y = element_text(size = 10, colour = 'black'),
        axis.title.x = element_text(face='bold', size=12),
        axis.title.y = element_text(face='bold', size=12))+
  xlab("Cell_pair") + ylab("ligand_receptor pair")

# plot circle -------------------------------------------------------------
library(tidyr)
library(dplyr)
library(iTALK)
library(circlize)
# circle_plot1 ------------------------------------------------------------

#挑选显著互作
df_sig <- df[df$pval <=0.05 & df$mean >= 2,]
df_sig <- df_sig %>% separate(celltype_pair, c("source", "target"), "\\|")
df_sig <- df_sig %>% separate(interacting_pair, into = c("ligand", "receptor"), sep = "_", extra = "merge")

#可以选择自己需要展示的受配体展示，挑选数据即可
#这里我直接按照mean排序，选择前40可视化
df_sig <- df_sig[order(-df_sig$mean),]
df_sig_plot <- df_sig[1:40,]

# colnames(df_sig)[3] <- "cell_from"
# colnames(df_sig)[4] <- "cell_to"
# 
# 
# df_sig <- df_sig %>%
#   select(1:2, cell_from_mean_exprs = 3, 4:ncol(.)) %>%
#   mutate(cell_from_mean_exprs = df_sig$mean)
# 
# 
# df_sig <- df_sig %>%
#   select(1:2, cell_from_mean_exprs = 3, 4:ncol(.)) %>%
#   mutate(cell_from_mean_exprs = df_sig$mean)




#设置颜色
gene_col<-structure(c(rep('#CC3333',length(df_sig_plot$ligand)),
                      rep("#006699",length(df_sig_plot$receptor))),
                    names=c(df_sig_plot$ligand,df_sig_plot$receptor))

cell_col <- structure(c("#d2981a", "#a53e1f", "#457277", "#8f657d", "#42819F", "#86AA7D", "#CBB396"),
                      names=unique(df_sig_plot$source))#设置细胞颜色



#弦图连线体现source cell，link_cols = T，不设置link.arr.col
ks_iTALK_LRPlot(df_sig_plot,#挑选前40作图
              link_cols = T,
              link.arr.lwd=df_sig_plot$mean,
              link.arr.width=0.1,
              link.arr.type = "triangle",
              link.arr.length = 0.1,
              # link.arr.col = alpha("#47B9B5",0.4),#连线颜色设置
              print.cell = T,
              track.height_1=uh(5, "mm"),
              track.height_2 = uh(10, "mm"),
              annotation.height_1=0.02,
              annotation.height_2=0.02,
              text.vjust = "0.1cm",
              gene_col = gene_col,
              cell_col = cell_col)


#弦图连线不体现source cell，link_cols = F，设置link.arr.col
ks_iTALK_LRPlot(df_sig_plot,#挑选前40作图
                link_cols = F,
                link.arr.lwd=df_sig_plot$mean,
                link.arr.width=0.1,
                link.arr.type = "triangle",
                link.arr.length = 0.1,
                link.arr.col = alpha("#47B9B5",0.4),#连线颜色设置
                print.cell = T,
                track.height_1=uh(5, "mm"),
                track.height_2 = uh(10, "mm"),
                annotation.height_1=0.02,
                annotation.height_2=0.02,
                text.vjust = "0.1cm",
                gene_col = gene_col,
                cell_col = cell_col)

# circle_plot2 ------------------------------------------------------------

#挑选自己感兴趣的细胞进行可视化，选择显著的互作
df_sig <- df[df$pval <=0.05,]
cell_inter = c('NK|CD14 Monocytes', 'NK|FCGR3A Monocytes')
LR = c('CD1D_LILRB2', 'CD44_TYROBP', 'CD48_CD244',"HLA-F_KIR3DL2")



ks_comm_chordDiagram(data = df_sig,
                     cell_inter = cell_inter,
                     LR=LR,
                     group=c("NK"='#0c0459',
                       "CD14 Monocytes"='#F09709',
                       "FCGR3A Monocytes"='#47B9B5'),
                     text_size=0.8)