test.txt:文件格式：
| gene | TCGA-3M-AB46-01A-11R-A414-31 | TCGA-3M-AB47-01A-22R-A414-31 | TCGA-B7-A5TK-01A-12R-A36D-31 | TCGA-B7-A5TN-01A-21R-A31P-31 |
| A2LD1 | 431 | 606 | 2731 | 194 |
| A2M | 35864 | 15942 | 22086 | 109363 |
| AADACL2 | 0 | 2 | 10 | 0 |
| AADAT | 643 | 388 | 317 | 241 |
pheno.txt文件格式：
TCGA-3M-AB46-01A-11R-A414-31 a
TCGA-3M-AB47-01A-22R-A414-31 b
TCGA-B7-A5TK-01A-12R-A36D-31 b
TCGA-B7-A5TN-01A-21R-A31P-31 a
TCGA-BR-6457-01A-21R-1802-13 a
TCGA-BR-6458-01A-11R-1802-13 a
TCGA-BR-6706-01A-11R-1884-13 b
## Librarys
library(limma)
## Matrix File
raw.data <-read.delim("test.txt")
attach(raw.data)
names(raw.data)
d <- raw.data[, 2:130]
rownames(d) <- raw.data[, 1]
# Pheno data file
pheno <- read.table("pheno.txt",header = F)
colnames(pheno) <- c("sample","group")
Group<-factor(pheno$group,levels=levels(pheno$group))
##To design matrix---
design<-model.matrix(~0+Group)
##Normalisation
y <- voom(d,design,plot=TRUE)
colnames(design)
fit <-lmFit(y,design)
##Designing Contrast Matrix for group Differentiation
cont.wt<-makeContrasts(Groupb-Groupa,levels=design)
fit2 <-contrasts.fit(fit,cont.wt)
fit3<-eBayes(fit2)
DE<-topTable(fit3,number = 'all')
write.csv(DE, "limma_genes_test.csv")
selected = rownames(DE[DE$P.Value <0.05,])
# 默認是holm方法控制FWER
esetSel = y[selected, ]
heatmap(esetSel@.Data[[1]])
#p<- DE[DE$P.Value <1,]$P.Value
#esetSela <- y[]
#p.adjust(c(0.001,0.02,0.03),"BH")