limma.voom.running: Implement voom with limma by default settings.

In lunge111/intSEQ: Differential Expression Analysis of RNA-seq Data with Integrated Likelihood Method

Description

Using voom to transform the data then pass the transformed data into limma pipeline.

Usage

limma.voom.running(count.data, cond, normalize= TRUE)

Arguments

count.data	A numeric matrix of the count table.
cond	A vector of factors or numeric of the predictor variable.
normalize	Logical, whether to use "TMM" normalization.

Details

The default method of normalization is "TMM" using the calcNormFactors. Then voom, lmFit and eBayes in limma package are called sequentially with default options.

Value

An object of class MArrayLM

Author(s)

Yilun Zhang, David Rocke

References

Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.

Examples

data(count.data)
data(condition)
pv.voom <- limma.voom.running(count.data,condition)

lunge111/intSEQ documentation built on May 20, 2019, 9:38 a.m.