Ubuntu
r-bioc-sva package

r-bioc-sva 3.50.0-1 source package in Ubuntu

r-bioc-sva (3.50.0-1) unstable; urgency=medium

  * New upstream version

 -- Andreas Tille <email address hidden>  Fri, 01 Dec 2023 06:58:08 +0100

Series	Pocket	Published	Component	Section
Oracular	release	on 2024-04-29	universe	misc
Noble	release	on 2023-12-15	universe	misc

File	Size	SHA-256 Checksum
r-bioc-sva_3.50.0-1.dsc	2.1 KiB	7ba30da6a33a844df97c622fb8e52b60d8eed5a537463e5249c32ba36f4cd19c
r-bioc-sva_3.50.0.orig.tar.gz	442.3 KiB	a11f635fc70f43c8c01613d8402eb6161608fc85bb4a633b56769778037f9f5c
r-bioc-sva_3.50.0-1.debian.tar.xz	5.8 KiB	e916ddb488d3226e3bf72a111907af35f23711e4447bf8280fe0b5f1a7e41faa

No changes file available.

r-bioc-sva: GNU R Surrogate Variable Analysis: The sva package contains functions for removing batch
effects and other unwanted variation in high-throughput
experiment. Specifically, the sva package contains functions
for the identifying and building surrogate variables for
high-dimensional data sets. Surrogate variables are covariates
constructed directly from high-dimensional data (like gene
expression/RNA sequencing/methylation/brain imaging data) that
can be used in subsequent analyses to adjust for unknown,
unmodeled, or latent sources of noise. The sva package can be
used to remove artifacts in three ways: (1) identifying and
estimating surrogate variables for unknown sources of variation
in high-throughput experiments (Leek and Storey 2007 PLoS
Genetics,2008 PNAS), (2) directly removing known batch
effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing
batch effects with known control probes (Leek 2014 biorXiv).
Removing batch effects and using surrogate variables in
differential expression analysis have been shown to reduce
dependence, stabilize error rate estimates, and improve
reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008
PNAS or Leek et al. 2011 Nat. Reviews Genetics).