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.
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
Maintainer: Sutirtha Chakraborty <email@example.com>
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.
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##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))
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