getPCA: Principle Component Analysis (PCA) in QSea

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

Principle Component Analysis (PCA) in QSea

Description

The getPCA() function performs a Principle Component Analysis (PCA) of the coverage profiles from a qsea object for exploratory data analysis.

Usage

getPCA(qs, chr= getChrNames(qs),ROIs, minRowSum=20, keep ,
    norm_method=normMethod(logRPM =
    c("log", "library_size", "cnv", "preference", "psC10")), topVar=1000,
    samples=getSampleNames(qs), minEnrichment = 0)

Arguments

qs	DIPSset (mandatory)
chr	chromosomes to consider
ROIs	If specified, only windows overlapping ROIs are considered.
minRowSum	minimal number of total read counts per window over all samples
keep	windows to consider
norm_method	name of predefined normalization (e.g. "beta"), or user defined normalization by calling normMethod() function
topVar	only the top variable windows are considered
samples	names of samples to be considered
minEnrichment	for transformation to absolute methylation level, you can specify the minimal number of expected reads for a fully methylated window. This avoids inaccurate estimates, due to low enrichment.

Details

The principle component analysis is calculated using the singular value decomposition (svd).

Value

getPCA() returns a list object, containing the svd and information on the selected windows.

Author(s)

Mathias Lienhard

See Also

plotPCA

Examples

qs=getExampleQseaSet( repl=5)
pca=getPCA(qs, norm_method="beta")
colors=c(rep("red", 5), rep("green", 5))
plotPCA(pca, bgColor=colors)
#plotPCAfactors is more interesting, if ROIs have been specified in getPCA
plotPCAfactors(pca)

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