SVAPLSseq-package: SVAPLSseq: An R package to adjust for the hidden factors of...

In SVAPLSseq: SVAPLSseq-An R package to estimate the hidden factors of unwanted variability and adjust for them to enable a more powerful and accurate differential expression analysis based on RNAseq data

Description

The package SVAPLSseq contains functions that are intended for the identification and correction of the hidden variability owing to a variety of unknown subject/sample specific and technical effects of residual heterogeneity in an RNAseq gene expression data.

Details

Package:	SVAPLSseq
Type:	Package
License:	GPL-3

The package can be used to find the genes that are truly differentially expressed between two groups of samples from an RNAseq data, after adjusting for different hidden factors of expression heterogeneity. The function svplsSurr operates on the raw data matrix of gene level read counts and extracts the signatures of the underlying hidden variability in the form of a set of surrogate variables. The function svplsTest detects the truly positive genes after correcting for the hidden signals (surrogate variables) extracted by svplsSurr.

Author(s)

Sutirtha Chakraborty.

Maintainer: Sutirtha Chakraborty <[e-mail]>

References

Boulesteix, A-L. and Strimmer, K. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Briefings in Bioinformatics 2007; 8(1):32–44.

See Also

svplsSurr, svplsTest

Examples

##Loading the Simulated Data
data(sim.dat)

## Fitting a linear model with the surrogate variables and detecting the differentially expressed genes
group = as.factor(c(rep(1, 10), rep(-1, 10)))
sv <- svplsSurr(dat = sim.dat, group = group, surr.select = "automatic")
surr = surr(sv)
fit <- svplsTest(dat = sim.dat, group = group, surr = surr, normalization = "TMM", test = "t-test")
head(sig.features(fit))

SVAPLSseq documentation built on April 28, 2020, 6:30 p.m.