nbinomTestForMatrices: Perform row-wise tests for differences between the base means...

In DESeq: Differential gene expression analysis based on the negative binomial distribution

Description

This function is called by nbinomTest. Call it directly only if the S4 interface is unsuitable for your task.

Usage

nbinomTestForMatrices(countsA, countsB, sizeFactorsA, sizeFactorsB, dispsA, dispsB )

Arguments

countsA	A matrix of counts, where each column is a replicate
countsB	Another matrix of counts, where each column is a replicate
sizeFactorsA	Size factors for the columns of the matrix 'countsA'
sizeFactorsB	Size factors for the columns of the matrix 'countsB'
dispsA	The dispersions for 'countsA', a vector with one value per gene
dispsB	The same for 'countsB'

Details

See the vignette for an exact description of the null hypothesis tested.

Value

A vector of unadjusted p values, one for each row in the counts matrices.

Author(s)

Simon Anders, [e-mail]

Examples

cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
cds <- estimateDispersions( cds, method="per-condition" )
colsA <- conditions(cds) == "A"
colsB <- conditions(cds) == "B"
bmvA <- getBaseMeansAndVariances( counts(cds)[,colsA], sizeFactors(cds)[colsA] )
bmvB <- getBaseMeansAndVariances( counts(cds)[,colsB], sizeFactors(cds)[colsB] )
pvals <- nbinomTestForMatrices( 
   counts(cds)[,colsA],
   counts(cds)[,colsB],
   sizeFactors(cds)[colsA],
   sizeFactors(cds)[colsB],
   fitInfo(cds,"A")$dispFunc( rowMeans( counts( cds, normalized=TRUE ) ) ),
   fitInfo(cds,"B")$dispFunc( rowMeans( counts( cds, normalized=TRUE ) ) ) )
names( pvals ) <- row.names( counts(cds) )
head( pvals )

# This here should give the same results:
head( nbinomTest( cds, "A", "B" )$pval )

DESeq documentation built on April 28, 2020, 6:37 p.m.