r-bioc-sva 3.42.0-1 source package in Ubuntu


r-bioc-sva (3.42.0-1) unstable; urgency=medium

  * Disable reprotest
  * New upstream version

 -- Andreas Tille <email address hidden>  Fri, 26 Nov 2021 08:18:45 +0100

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File Size SHA-256 Checksum
r-bioc-sva_3.42.0-1.dsc 2.1 KiB 7dcaed5719dd742dfc1b0541001f8edda1d85f8c09986201797b3e56878b9e94
r-bioc-sva_3.42.0.orig.tar.gz 430.8 KiB b2d5b33a2709e54de9c493a554180dd2c63145181ddbbd0a7b0e8bd8528b9f32
r-bioc-sva_3.42.0-1.debian.tar.xz 5.7 KiB cfc2672b497802d87be1a96f5d49aa42995770747b1db94cdeb6ed13ea416196

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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).

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