该文献的题目是：Identifying disease genes and module biomarkers by differential interactions
本文受益于R的开源，受限于本人的能力和水平，也没有在github等平台上找到作者的代码，故通过综合各种信息写了这些代码，至少我自己用R最新版是能跑出来的。复现一篇2012年生物网络标志物的文献（3）

第四步之前的疾病对照组结合获得的基因

rm(list = ls())
load(file = "step2output.Rdata")
load(file = "step4output.Rdata")
install.packages(c("STRINGdb"),ask = F,update = F,force = TRUE)
library(STRINGdb)
remain_early1 <- c("symbol",rownames(early_phase_control))  #获得早期控制组需要保留的样本
expr_early_contol <- symbol2express[,colnames(symbol2express) %in% remain_early1]
#从基因表达和symbol里得到需要保留的早期control列

remain_early2 <- c("symbol",rownames(early_phase_disease))  
expr_early_disease <- symbol2express[,colnames(symbol2express) %in% remain_early2]

remain_middle1 <- c("symbol",rownames(middle_phase_control))  
expr_middle_control <- symbol2express[,colnames(symbol2express) %in% remain_middle1]

remain_middle2 <- c("symbol",rownames(middle_phase_disease))  
expr_middle_disease <- symbol2express[,colnames(symbol2express) %in% remain_middle2]

remain_late1 <- c("symbol",rownames(late_phase_control))  
expr_late_control <- symbol2express[,colnames(symbol2express) %in% remain_late1]

remain_late2 <- c("symbol",rownames(late_phase_disease))  
expr_late_disease <- symbol2express[,colnames(symbol2express) %in% remain_late2]



save(expr_early_contol,expr_early_disease,expr_late_control,expr_late_disease,expr_middle_control,expr_middle_disease,file = "step5output.Rdata")

表达和基因名