ABSSeq (version 1.8.0)

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

Description

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

Usage

ABSSeq(object, adjmethod = "BH", replaceOutliers = TRUE, 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.

quiet

default is FALSE, whether to print messages at each step

...

parameters passed to ReplaceOutliersByMAD from callParameter

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.

Details

The differential expression analysis models the total counts difference by a Negative binomal distribution $$NB(\mu,r)$$:

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)