DEseq2 PCA plot

>library(DESeq2)
>raw_count_filt<-read.table("HTseq.QC.sort.by.n.count",header = T,row.names = 1)

> head(raw_count_filt)
              KKO.mLtb1 KO.muta KO.mutc KO.WTa KO.WTb KO.WTc
0610006L08Rik         1       0       0      0      0      0
0610007P14Rik       999    1234    1293   1234   1663   1270
0610009B22Rik       359     393     274    381    385    288
0610009E02Rik         4       6       1      6      3      4
0610009L18Rik         8       3       7     11      6     10
0610009O20Rik       635     550     561    564    692    493

#构建dds对象
condition <- factor(c('treated','treated','treated','untreated','untreated','untreated'))
dds <- DESeqDataSetFromMatrix(countData, DataFrame(condition), design= ~ condition )
> dds
class: DESeqDataSet 
dim: 48587 6 
metadata(1): version
assays(3): counts mu cooks
rownames(48587): 0610006L08Rik
  0610007P14Rik ... n-TSaga9 n-TStga1
rowData names(27): baseMean baseVar ...
  deviance maxCooks
colnames(6): KKO.mLtb1 KO.muta ... KO.WTb
  KO.WTc
colData names(2): condition sizeFactor

#数据标准化处理
rld <- rlogTransformation(dds)   #DEseq2自己的方法标准化数据`
exprSet_new=assay(rld)   #提取DEseq2标准化后的数据
write.table(exprSet_new, file="FZH.DESeq2.normalization.txt", sep="\t",quote=F)
> head(exprSet_new)
              KKO.mLtb1   KO.muta   KO.mutc    KO.WTa    KO.WTb    KO.WTc
0610006L08Rik -1.967768 -1.970802 -1.970600 -1.970944 -1.970991 -1.970597
0610007P14Rik 10.076339 10.268512 10.434751 10.183503 10.430670 10.419946
0610009B22Rik  8.440090  8.509859  8.344399  8.417481  8.402646  8.380791
0610009E02Rik  1.971115  1.985315  1.951507  1.981837  1.961226  1.975130
0610009L18Rik  2.902465  2.867296  2.902476  2.917000  2.883140  2.925268
0610009O20Rik  9.237139  9.120903  9.246608  9.065483  9.208009  9.142025

plotPCA(rld, intgroup=c('condition')) #DEseq2自带函数
dev.copy(png,'Deseq2_pca.png')
dev.off()

Deseq2_pca.png

通过上图，可以看见组层次样本相似信息，无法精确到具体是哪一个样本？如果想要精确到具体是哪一个样本（图中点各自代表哪一个样本），该怎么做呢？

>pcaData <- plotPCA(rld, intgroup=c("condition"), returnData=TRUE)
> pcaData
                 PC1        PC2     group condition      name
KKO.mLtb1 -9.3479355 -0.4078916   treated   treated KKO.mLtb1
KO.muta    2.3993577  4.0581652   treated   treated   KO.muta
KO.mutc    0.8091405  2.3055847   treated   treated   KO.mutc
KO.WTa     1.0457699  0.2362714 untreated untreated    KO.WTa
KO.WTb     2.1439032 -3.2297884 untreated untreated    KO.WTb
KO.WTc     2.9497641 -2.9623413 untreated untreated    KO.WTc

plot(pcaData[,1:2],pch=19,col=c("red","red","red","blue","blue","blue"))
text(pcaData[,1],pcaData[,2]+0.2,row.names(pcaData),cex=0.5)

FZH.deseq2_pca.name.png