independentFiltering: Perform independent filtering in differential expression...

In statOmics/zingeR: Zero-Inflated Negative binomial Gene Expression in R

Description

This function uses the DESeq2 independent filtering method to increase detection power in high throughput gene expression studies.

Usage

independentFiltering(object, filter, objectType = c("edgeR", "limma"))

Arguments

object	Either a DGELRT-class object or a data.frame with differential expression results.
filter	The characteristic to use for filtering, usually a measure of normalized mean expression for the features.
objectType	Either "edgeR" or "limma". If "edgeR", it is assumed that object is of class DGELRT-class, the output of glmLRT. If "limma", it is assumed that object is a data.frame and the output of a limma-voom analysis.

References

Michael I Love, Wolfgang Huber, and Simon Anders. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12):550, dec 2014.

See Also

results

Examples

library(limma)
data(islamEset,package="zingeR")
islam=exprs(islamEset)[1:2000,]
design=model.matrix(~pData(islamEset)[,1])
d=DGEList(islam)
nf=calcNormFactors(islam)
y=zeroWeightedVoom(d,design,nf=nf,maxit=200)
fit=lmWeightedFit(y,design)
fit=eBayes(fit)
tt=topTable(fit,coef=2,sort.by="none",number=nrow(fit))
baseMean=unname(rowMeans(sweep(d$counts,2,nf,FUN="*")))
ttFiltered=independentFiltering(tt,filter=baseMean, objectType="limma")

statOmics/zingeR documentation built on May 20, 2019, 6:48 p.m.