addLibraryFactors: Estimate effective library size

In MatthiasLienhard/qsea: IP-seq data analysis and vizualization

Estimate effective library size

Description

Normalization factors for effective library size are computed using the trimmed mean of m-values approach (TMM).

Usage

addLibraryFactors(qs, factors,...)

Arguments

qs	The qseaSet object
factors	In case normalization factors have been pre-computed by the user, they can be passed with this parameter. In this case QSEA adds this factors to the qseaSet object and does not compute normalization factors.
...	Further parameters used for the TMM normalization (see details)

Details

The user can specify the TMM normalization by setting the following additional parameters, which are passed to the internal functions. \trimA [default: c(.5,.99)] lower and upper quantiles for trimming of A values \trimM [default: c(.1,.9)] lower and upper quantiles for trimming of M values \doWeighting [default: TRUE] computes a weighted TMM \ref [default: 1] the index of the reference sample \plot [default: FALSE] if set to TRUE, MvsA plots depicting the TMM normalization are created. \nReg [default: 500000] Number of regions to be analyzed for normalization. Regions are drawn uniformly over the whole genome.

Value

This function returns the qseaSet object, containing effective library size normalization factors.

Author(s)

Mathias Lienhard

See Also

edgeR::calcNormFactors

Examples

qs=getExampleQseaSet(expSamplingDepth=500*10^(1:5), repl=5)
#in this case, the first sample has only view reads, so it is important to set
#the reference sample
qs=addLibraryFactors(qs, plot=TRUE, ref="Sim5N")

MatthiasLienhard/qsea documentation built on March 22, 2023, 1:15 p.m.