ABSSeq: Differential expression analysis based on the total counts...

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

Description

This function performs a default analysis by calling, in order, the functions: normalFactors, callParameter, callDEs.

Usage

ABSSeq(object, adjmethod = "BH", replaceOutliers = TRUE, useaFold = FALSE,
  quiet = FALSE, ...)

Arguments

object	an ABSDataSet object, contains the reads count matrix, groups and normalization method.
adjmethod	defualt is 'BH', method for p-value adjusted, see p.adjust.methods for details
replaceOutliers	default is TRUE, switch for outlier replacement.
useaFold	defualt is FALSE, switch for DE detection through fold-change, see callDEs for details
quiet	default is FALSE, whether to print messages at each step
...	parameters passed to ReplaceOutliersByMAD and genAFold from callParameter

Details

The differential expression analysis models the total counts difference by a Negative binomal distribution

NB(μ,r)

:

Value

an ABSDataSet object with additional elements, which can be retrieved by results: Amean and Bmean, mean of log2 normalized reads count for group A and B, foldChange, shrinked (expression level and gene-specific) log2 of fold-change, B - A, rawFC, raw log2 of fold-change, B-A (without shrinkage), lowFC, expression level corrected log2 fold-change, pvalue, pvalue from NB distribution model, adj.pvalue, adjuested p-value used p.adjust method.

Author(s)

Wentao Yang

References

Wentao Yang, Philip Rosenstiel & Hinrich Schulenburg: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences

Examples

data(simuN5)
obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups))
obj <- ABSSeq(obj)
res <- results(obj,c("Amean","Bmean","foldChange","pvalue","adj.pvalue"))
head(res)

ABSSeq documentation built on Nov. 8, 2020, 5:07 p.m.