pfNbinomTest {Polyfit} | R Documentation |
Polyfit extensions to the DESeq functions nbinomTest
and nbinomTestForMatrices
which test for differences between the base means of two conditions (i.e., for differential expression in the case of RNA-Seq).
pfNbinomTest(cds, condA, condB, pvals_only = FALSE, eps = NULL) pfNbinomTestForMatrices(countsA, countsB, sizeFactorsA, sizeFactorsB, dispsA, dispsB )
cds |
a CountDataSet with size factors and raw variance functions |
condA |
one of the conditions in 'cds' |
condB |
another one of the conditions in 'cds' |
pvals_only |
return only a vector of (unadjusted) p values instead of the data frame described below |
eps |
This argument is no longer used. Do not use it |
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' |
These functions have the same behaviour as the DESeq functions nbinomTest
and nbinomTestForMatrices
, except that the ‘flagpole’ in the P-value histogram, particularly at p = 1 is redistributed using the function twoSidedPValueFromDiscrete
.
pfNbinomTest
gives a data frame with the following columns:
id |
The ID of the observable, taken from the row names of the counts slots. |
baseMean |
The base mean (i.e., mean of the counts divided by the size factors) for the counts for both conditions |
baseMeanA |
The base mean (i.e., mean of the counts divided by the size factors) for the counts for condition A |
baseMeanB |
The base mean for condition B |
foldChange |
The ratio meanB/meanA |
log2FoldChange |
The log2 of the fold change |
pval |
The p value for rejecting the null hypothesis 'meanA==meanB' |
padj |
The adjusted p values (adjusted with 'p.adjust( pval, method="BH")') |
pfNbinomTestForMatrices
gives a vector of unadjusted p values, one for each row in the counts matrices.
Conrad Burden, conrad.burden@anu.edu.au, based on software by Simon Anders
Burden, C.J., Qureshi, S. and Wilson, S.R. (2014). Error estimates for the analysis of differential expression from RNA-seq count data, PeerJ PrePrints 2:e400v1.
Anders, S. and Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biology, 11(10), R106.
cds <- makeExampleCountDataSet() cds <- estimateSizeFactors( cds ) cds <- estimateDispersions( cds ) nbT <- nbinomTest( cds, "A", "B" ) head( nbT ) nbTPolyfit <- pfNbinomTest( cds, "A", "B" ) head( nbTPolyfit ) oldpar <- par(mfrow=c(1,2)) hist(nbT$pval,breaks=seq(0,1,by=0.01), xlab="P-value", main="DESeq") hist(nbTPolyfit$pval,breaks=seq(0,1,by=0.01), xlab="P-value", main="polyfit-DESeq") par(oldpar)