stat.limma: Statistical testing with limma

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 limma statistical testing. It accepts a matrix of normalized gene counts or an S4 object specific to each normalization algorithm supported by metaseqR.

Usage

    stat.limma(object, sample.list, contrast.list = NULL,
        stat.args = NULL)

Arguments

object	a matrix or an object specific to each normalization algorithm supported by metaseqR, containing normalized counts. Apart from matrix (also for NOISeq), the object can be a SeqExpressionSet (EDASeq), CountDataSet (DESeq) or DGEList (edgeR).
sample.list	the list containing condition names and the samples under each condition.
contrast.list	a named structured list of contrasts as returned by make.contrast.list or just the vector of contrasts as defined in the main help page of metaseqr.
stat.args	a list of edgeR statistical algorithm parameters. See the result of get.defaults("statistics", "limma") for an example and how you can modify it.

Value

A named list of p-values, whose names are the names of the contrasts.

Author(s)

Panagiotis Moulos

Examples

require(DESeq)
data.matrix <- counts(makeExampleCountDataSet())
sample.list <- list(A=c("A1","A2"),B=c("B1","B2","B3"))
contrast <- "A_vs_B"
norm.data.matrix <- normalize.edger(data.matrix,sample.list)
p <- stat.limma(norm.data.matrix,sample.list,contrast)

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