搜索
查看: 85|回复: 0

[annotation] DESeq2包

[复制链接]

25

主题

25

帖子

127

积分

管理员

Rank: 9Rank: 9Rank: 9

积分
127
发表于 2018-9-13 17:56:15 | 显示全部楼层 |阅读模式
1)简介:
DESeq2-package: for differential analysis of count data(对count data 做差异分析)
2)安装
[Python] 纯文本查看 复制代码
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:
[Python] 纯文本查看 复制代码
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:
[Python] 纯文本查看 复制代码
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

[Python] 纯文本查看 复制代码
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())
[Python] 纯文本查看 复制代码
# 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")

[Python] 纯文本查看 复制代码
dds <- makeExampleDESeqDataSet(m=6,betaSD=1)
rld <- rlog(dds)
dists <- dist(t(assay(rld)))
[/color][color=Black]plot(hclust(dists))


3.9)plotPCA(Sample PCA plot for transformed data)
用法:plotPCA(object, intgroup = "condition",ntop = 500, returnData = FALSE)
[Python] 纯文本查看 复制代码
# 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 ) )









本帖子中包含更多资源

您需要 登录 才可以下载或查看,没有帐号?立即注册

x
回复

使用道具 举报

您需要登录后才可以回帖 登录 | 立即注册

本版积分规则

QQ|手机版|小黑屋|生信技能树    

GMT+8, 2018-9-22 04:32 , Processed in 0.143868 second(s), 27 queries .

Powered by Discuz! X3.2

© 2001-2013 Comsenz Inc.