ABSSeq {ABSSeq} | R Documentation

## 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, 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)

[Package ABSSeq version 1.46.0 Index]