偷偷问一下，关注了吗？
内容获取
1、购买打包合集（《KS科研分享与服务》付费内容打包集合），价格感人，可以加入微信VIP群（答疑交流群，甚至有小伙伴觉得群比代码更好），可以获取建号以来所有内容，群成员专享视频教程，提前更新，其他更多福利！
2、《KS科研分享与服务》公众号有QQ群，进入门槛是20元（完全是为了防止白嫖党，请理解），请考虑清楚。群里有免费推文的注释代码和示例数据（终身拥有），没有付费内容，群成员福利是购买单个付费内容半价！
需要者详情请联系作者（非需要者勿扰，处理太费时间）：
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$source))#设置细胞颜色#弦图连线体现source cell，link_cols = T，不设置link.arr.colks_iTALK_LRPlot(df_sig_plot,#挑选前40作图link_cols = T,link.arr.lwd=df_sig_plot$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.colks_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)
觉得我们分享有些用的，点个赞再走呗！
关注我们获取精彩内容：
关注不迷路：扫描下面二维码关注公众号！
B站视频号链接：https://space.bilibili.com/471040659?spm_id_from=333.1007.0.0
关注 KS科研分享与服务，
认清正版优质内容和服务！
优质内容持续输出，物超所值！
合作联系：ks_account@163.com
