Nature｜100万细胞的单细胞多组学——学习生信分析能得出哪些结论——代码宝库

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不设置🌟有时会收不到公众号内容，code一段时间后会失效,今日代码在 这篇文章的代码简直是多组学学习的宝库，整理的规规整整。多组学学习，就看这篇。 举个下图的例子（主图 Figure2a） 看看这种能得出什么结论 library(data.table) library(scales) library(ggplot2) df1.full.mg = fread( "~/Documents/Research/t21-proj/out/full/DEG_list/liver_v_femur.txt" ,data.table = F,stringsAsFactors = F) genes_of_interest < - c( "TOP2A" , "CDC20" , "BIRC5" , "CXCR4" , "IL6R" , "GATA1" , "TAL1" , "FOXO3" , "KLF1" , "AHSP" , "ITGA2B" , "SERPINE2" , "FOXO3" , "APOE" , "DUSP1" ) df1.full.mg$geneLabel < - NA df1.full.mg$geneLabel[df1.full.mg$names %in% genes_of_interest] < - df1.full.mg$names[df1.full.mg$names %in% genes_of_interest] tmp1 < - df1.full.mg[,c( "logFC.h" , "P.Value.h" , "class" , 'names' )] tmp2 < - df1.full.mg[,c( "logFC.t21" , "P.Value.t21" , "class" , 'names ………………………………