normalize.deseq: Normalization based on the DESeq package

In pmoulos/metaseqr: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms.

Description

This function is a wrapper over DESeq normalization. It accepts a matrix of gene counts (e.g. produced by importing an externally generated table of counts to the main metaseqr pipeline).

Usage

    normalize.deseq(gene.counts, sample.list,
        norm.args = NULL, output = c("matrix", "native"))

Arguments

gene.counts	a table where each row represents a gene and each column a sample. Each cell contains the read counts for each gene and sample. Such a table can be produced outside metaseqr and is imported during the basic metaseqr workflow.
sample.list	the list containing condition names and the samples under each condition.
norm.args	a list of DESeq normalization parameters. See the result of get.defaults("normalization", "deseq") for an example and how you can modify it.
output	the class of the output object. It can be "matrix" (default) for versatility with other tools or "native" for the DESeq native S4 object (CountDataSet). In the latter case it should be handled with suitable DESeq methods.

Value

A matrix or a CountDataSet with normalized counts.

Author(s)

Panagiotis Moulos

Examples

require(DESeq)
data.matrix <- counts(makeExampleCountDataSet())
sample.list <- list(A=c("A1","A2"),B=c("B1","B2","B3"))
diagplot.boxplot(data.matrix,sample.list)

norm.data.matrix <- normalize.deseq(data.matrix,sample.list)
diagplot.boxplot(norm.data.matrix,sample.list)

pmoulos/metaseqr documentation built on Dec. 29, 2020, 5:56 a.m.