Description Usage Arguments Details Value Author(s) References Examples
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).
1 2 | 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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)
|
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