results: Accessor functions for the result from a ABSDataSet

In wtaoyang/ABSSeq: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences

Description

Accessor functions for the result from a ABSDataSet by given names

Usage

## S4 method for signature 'ABSDataSet'
results(object, cnames = c("Amean", "Bmean",
  "baseMean", "absD", "Variance", "rawFC", "lowFC", "foldChange", "pvalue",
  "adj.pvalue", "trimmed"))

Arguments

object

a ABSDataSet

cnames

a vecotr of names for output, which are among: 'Amean','Bmean', log2 of mean counts for group A and B, "baseMean', estimated mean for absolute counts difference (absD), used for mu in pnbinom 'absD', absolute counts difference in total 'Variance', pooled Variance for two groups 'rawFC','lowFC', 'foldChange', log2 fold-change of original (Bmean-Amean), corrected by expression level and corrected by both expression level and gene-specific dispersion 'pvalue','adj.pvalue', pvalue and adjusted pvalue 'trimmed', number of trimmed outliers

Details

This function returns the result of ABSSeq as a table or a vector depended on the given names, see ABSSeq

Value

a table according to canmes.

See Also

ABSSeq

Examples

data(simuN5)
obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups))
obj <- normalFactors(obj)
obj <- callParameter(obj)
obj <- callDEs(obj)
head(results(obj))

wtaoyang/ABSSeq documentation built on May 27, 2019, 8:46 a.m.