independentFiltering: Perform independent filtering in differential expression...

In zinbwave: Zero-Inflated Negative Binomial Model for RNA-Seq Data

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.

Author(s)

Koen Van den Berge

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

zinbwave documentation built on Nov. 8, 2020, 8:11 p.m.