DESeq2包

linux从零开始

1）简介：
DESeq2-package： for differential analysis of count data（对count data 做差异分析）
2）安装

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if("DESeq2" %in% rownames(installed.packages()) == FALSE) {source("http://bioconductor.org/biocLite.R");biocLite("DESeq2")}
suppressMessages(library(DESeq2))
ls('package:DESeq2')

3）对象的简单使用
3.1）coef（Extract a matrix of model coefficients/standard errors，高级用户检验模型系数）
语法：coef(object, SE = FALSE, ...)
参数解释：
object：a DESeqDataSet returned by DESeq, nbinomWaldTest, or nbinomLRT.
例子：

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dds <- makeExampleDESeqDataSet(m=4)
dds <- DESeq(dds)
coef(dds)[1,]
coef(dds, SE=TRUE)[1,]

3.2） collapseReplicates：Collapse technical replicates in a RangedSummarizedExperiment or DESeqDataSet(用于消除技术重复)
用法：collapseReplicates(object, groupby, run, renameCols = TRUE)
参数：
object：A RangedSummarizedExperiment or DESeqDataSet
groupby：a grouping factor, as long as the columns of object，分组因子
run：optional, the names of each unique column in object. if provided, a new column runsCollapsed will be added to the colData which pastes together the names of run （测序run）
renameCols：whether to rename the columns of the returned object using the levels of the grouping factor
例子：

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dds <- makeExampleDESeqDataSet(m=12)
str(dds)
dds$sample <- factor(sample(paste0("sample",rep(1:9, c(2,1,1,2,1,1,2,1,1)))))
dds$run <- paste0("run",1:12) 
ddsColl <- collapseReplicates(dds, dds$sample, dds$run)
# examine the colData and column names of the collapsed data
colData(ddsColl)
colnames(ddsColl)
# check that the sum of the counts for "sample1" is the same
# as the counts in the "sample1" column in ddsColl
matchFirstLevel <- dds$sample == levels(dds$sample)[1]
stopifnot(all(rowSums(counts(dds[,matchFirstLevel])) == counts(ddsColl[,1])))

3.3）counts：Accessors for the ’counts’ slot of a DESeqDataSet object
语法:counts(object, normalized = FALSE,replaced = FALSE)
参数:
object:a DESeqDataSet object.
normalized：logical indicating whether or not to divide the counts by the size factors or normalization factors before returning
replaced：返回极端值

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dds <- makeExampleDESeqDataSet(m=4) 
head(counts(dds))
dds <- estimateSizeFactors(dds)  
head(counts(dds, normalized=TRUE))

3.4）DESeq：Differential expression analysis based on the Negative Binomial
语法：
DESeq(object, test = c("Wald", "LRT"), fitType = c("parametric", "local","mean"), sfType = c("ratio", "poscounts", "iterate"), betaPrior,full = design(object), reduced, quiet = FALSE,minReplicatesForReplace = 7, modelMatrixType, useT = FALSE, minmu = 0.5,parallel = FALSE, BPPARAM = bpparam())

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# count tables from RNA-Seq data
cnts <- matrix(rnbinom(n=1000, mu=100, size=1/0.5), ncol=10)
cond <- factor(rep(1:2, each=5))
# object construction
dds <- DESeqDataSetFromMatrix(cnts, DataFrame(cond), ~ cond)
 
# standard analysis
dds <- DESeq(dds)
res <- results(dds)
 
# moderated log2 fold changes
resultsNames(dds)
resLFC <- lfcShrink(dds, coef=2, type="apeglm")
 
# an alternate analysis: likelihood ratio test
ddsLRT <- DESeq(dds, test="LRT", reduced= ~ 1)
[/color][color=#000000]resLRT <- results(ddsLRT)

3.8）rlog Apply a ’regularized log’ transformation
用法：
rlog(object, blind = TRUE, intercept, betaPriorVar, fitType = "parametric")
rlogTransformation(object, blind = TRUE, intercept, betaPriorVar,fitType = "parametric")

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dds <- makeExampleDESeqDataSet(m=6,betaSD=1)
rld <- rlog(dds)
dists <- dist(t(assay(rld)))
[/color][color=Black]plot(hclust(dists))

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3.9）plotPCA（Sample PCA plot for transformed data）
用法：plotPCA(object, intgroup = "condition",ntop = 500, returnData = FALSE)

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# using rlog transformed data:
dds <- makeExampleDESeqDataSet(betaSD=1)
rld <- rlog(dds)
plotPCA(rld)
# also possible to perform custom transformation:
dds <- estimateSizeFactors(dds)
# shifted log of normalized counts
se <- SummarizedExperiment(log2(counts(dds, normalized=TRUE) + 1),
colData=colData(dds))
# the call to DESeqTransform() is needed to
# trigger our plotPCA method.
plotPCA( DESeqTransform( se ) )

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