SVAPLSseq-package: SVAPLSseq: An R package to adjust for the hidden factors of...
In sutigit21/SVAPLSseq: SVAPLSseq-An R package to adjust for the hidden factors of variability in differential gene expression studies based on RNAseq data
Description
Details
Author(s)
References
See Also
Examples
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 <statistuta@gmail.com>
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
Examples
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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 = surr(sv)
fit <- svplsTest(dat = sim.dat, group = group, surr = surr, test = "Wald")
head(sig.genes(fit))
sutigit21/SVAPLSseq documentation built on May 30, 2019, 8:43 p.m.
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Description Details Author(s) References See Also Examples
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 <statistuta@gmail.com>
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
Examples
1 2 3 4 5 6 7 8 9 | ##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 = surr(sv)
fit <- svplsTest(dat = sim.dat, group = group, surr = surr, test = "Wald")
head(sig.genes(fit))
|
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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