DESeq是一个基于Reads count来进行差异分析的R包，具体安装与使用方法如下：

#Install DESeq package:

source("https://bioconductor.org/biocLite.R")

biocLite("DESeq")

#Load the DESeq package into the R environment:

library("DESeq")

#Use the read.delim function to read the count table into the R environment:

countTable <- read.delim("xxx.txt",row.names=1)

#Remove HTSeq-count special counters (last 5 rows of the count table):

##countTable <- countTable[-((dim(countTable)[1]-4):dim(countTable)[1]),] #Don't need(if the reads count is not from HTSeq result);

#Create a conditions factor variable, which indicates the treatment type for each sample:

conditions <- factor(c("CON","CON","CON","HFD","HFD","HFD"))

#Create a CountDataSet data structure, which stores all of the sample attributes that the DESeq package requires for computing differential expression:

cds <- newCountDataSet(countTable,conditions)

#Normalization is a critical step prior to testing for differential expression. DESeq contains the function, estimateSizeFactors, which uses the median count ratio method to compute scaling factors for each sample:

cds <- estimateSizeFactors(cds)

#Next, the variance for each gene needs to be estimated using the estimateDispersions function:

cds <- estimateDispersions(cds)

#Test for differential expression between "CON" and "HFD" samples using nbinomTest function:

res <- nbinomTest(cds,"CON","HFD")

#Export results to a table, which can be easily viewed as a spreadsheet:

write.csv(res,file="topTable.csv")

#Identify the gene IDs that correspond to differentially expressed genes (in this case we classify genes with an adjusted p--‐value < 0.05 as differentially expressed):

counts(cds,normalized=TRUE)[which(res$padj<0.05),]

#Close R using the following command:

q()

DESeq.txt