nbinomTestForMatrices {DESeq} | R Documentation |
This function is called by nbinomTest
. Call it directly only
if the S4 interface is unsuitable for your task.
nbinomTestForMatrices(countsA, countsB, sizeFactorsA, sizeFactorsB, dispsA, dispsB )
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' |
See the vignette for an exact description of the null hypothesis tested.
A vector of unadjusted p values, one for each row in the counts matrices.
Simon Anders, sanders@fs.tum.de
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 )