【PBD-176】凌辱で感じる美しきマゾ女優たち8時間 Bulk RNA(普通转录组)多组各异基因分析函数（视频教程）
图片【PBD-176】凌辱で感じる美しきマゾ女優たち8時間
然后咱们测试一下：率先是两组等叠加样本，很简单！
setwd('D:\\KS名堂\\公众号著述\\Bulk多组各异基因分析函数')#一、两组分析（样本数颠倒）-------------------------------------------------------------------df1 <- read.csv("Two_group.csv", header = T, row.names = 1)colnames(df1)# [1] "Cancer1" "Cancer2" "Cancer3" "Health1" "Health2" "Health3"meta1 <- data.frame(Cancer=c("Cancer1" ,"Cancer2" ,"Cancer3"), Health=c("Health1", "Health2", "Health3"))deg1_Deseq2 <- KS_bulkRNA_MultiGroup_DEGs(exprSet = df1, meta = meta1, methods = "DESeq2", test = "Cancer", control = "Health", repNum1 = 3, repNum2 = 3)deg1_edgeR <- KS_bulkRNA_MultiGroup_DEGs(exprSet = df1, meta = meta1, methods = "edgeR", test = "Cancer", control = "Health", repNum1 = 3, repNum2 = 3)deg1_limma <- KS_bulkRNA_MultiGroup_DEGs(exprSet = df1, meta = meta1, methods = "limma", test = "Cancer", control = "Health", repNum1 = 3, repNum2 = 3)叠加数不等的两组样分内析亦然如斯【PBD-176】凌辱で感じる美しきマゾ女優たち8時間，只有诞生好test和control即可：
色播五月#二、两组分析（样本数不同）-------------------------------------------------------------------df4 <- read.csv("two_diff_group.csv", header = T, row.names = 1)colnames(df4)# [1] "HC_1" "HC_2" "HC_3" "HC_4" "PPC_1" "PPC_2" "PPC_3" "PPC_4" "PPC_5" "PPC_6"meta4 <- data.frame(HC=c("HC_1","HC_2","HC_3","HC_4","",""), PPC=c("PPC_1","PPC_2","PPC_3","PPC_4","PPC_5","PPC_6"))deg4_Deseq2 <- KS_bulkRNA_MultiGroup_DEGs(exprSet = df4, meta = meta4, methods = "DESeq2", test = "PPC", control = "HC", repNum1 = 6, repNum2 = 4)deg4_edgeR <- KS_bulkRNA_MultiGroup_DEGs(exprSet = df4, meta = meta4, methods = "edgeR", test = "PPC", control = "HC", repNum1 = 6, repNum2 = 4)deg4_limma <- KS_bulkRNA_MultiGroup_DEGs(exprSet = df4, meta = meta4, methods = "limma", test = "PPC", control = "HC", repNum1 = 6, repNum2 = 4)咱们不错对比下三种要道各异基因遵守，发现细目不是透彻同样，那是因为算法不同样，无须纠结。在咱们这个数据内部不错看出，DEseq2和edgeR的遵守重合照旧挺多的。
#韦恩图deg4_Deseq2_sig <- deg4_Deseq2[which(abs(deg4_Deseq2$log2FoldChange)>0 & deg4_Deseq2$pvalue <=0.05),]deg4_edgeR_sig <- deg4_edgeR[which(abs(deg4_edgeR$logFC)>0 & deg4_edgeR$PValue <=0.05),]deg4_limma_sig <- deg4_limma[which(abs(deg4_limma$logFC)>0 & deg4_limma$P.Value <=0.05),]library(ggvenn)library(tidyverse)Venn_list <- list(deg4_Deseq2_sig=rownames(deg4_Deseq2_sig), deg4_edgeR_sig=rownames(deg4_edgeR_sig), deg4_limma_sig=rownames(deg4_limma_sig))#使用list_to_data_frame将list升沉为data.framedata_veen = list_to_data_frame(Venn_list)ggvenn(Venn_list, show_percentage = T, show_elements = F, text_size=3, digits = 1, set_name_size=4, stroke_color = "grey30", fill_color = c("#FF8C00","#4DAF4A","#B64E89"), set_name_color = c("#FF8C00","#4DAF4A","#B64E89"))图片
多组样本的分析就更简单了：大多诞生齐诞生好了。这里需要强调少量，那便是关于多组样本，在进行meta诞生的手艺，test组靠前，control靠后！
#四、多组test------------------------------------------------------------------df3 <- read.csv("count_gene.csv", header = T, row.names = 1, check.names = F)colnames(df3)# [1] "Fbrain 1" "Fbrain 2" "Fbrain 3" "Fbrain 4" "Fhom 1" "Fhom 2" "Fhom 3" # [8] "Mbrain 1" "Mbrain 2" "Mbrain 3" "Mbrain 4" "Mbrain 5" "Mhom 1" "Mhom 2" # [15] "Mhom 3" "Mhom 4" meta3 <- data.frame(Mhom = c("Mhom 1","Mhom 2", "Mhom 3","Mhom 4",""), Fhom = c("Fhom 1","Fhom 2","Fhom 3","",""), Mbrain=c("Mbrain 1","Mbrain 2","Mbrain 3","Mbrain 4","Mbrain 5"), Fbrain=c("Fbrain 1","Fbrain 2","Fbrain 3","Fbrain 4",""))deg3_Deseq2 <- KS_bulkRNA_MultiGroup_DEGs(exprSet = df3, meta = meta3, methods = "DESeq2", separator=" ")deg3_edgeR <- KS_bulkRNA_MultiGroup_DEGs(exprSet = df3, meta = meta3, methods = "edgeR", separator=" ")deg3_limma <- KS_bulkRNA_MultiGroup_DEGs(exprSet = df3, meta = meta3, methods = "limma", separator=" ")图片
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