r-bioc-sva 3.48.0-1 source package in Ubuntu


r-bioc-sva (3.48.0-1) unstable; urgency=medium

  * New upstream version
  * Standards-Version: 4.6.2 (routine-update)

 -- Andreas Tille <email address hidden>  Thu, 27 Jul 2023 15:19:31 +0200

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Medium Urgency

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File Size SHA-256 Checksum
r-bioc-sva_3.48.0-1.dsc 2.1 KiB 8085f76a7ef64208fd2124a458cc4d91d63a21716278c75601814d4eb6fefb92
r-bioc-sva_3.48.0.orig.tar.gz 442.7 KiB e3a58025603ddb943334b8b897b5f6aaa33e93dc5cbbf6053b560beb4e51d1b8
r-bioc-sva_3.48.0-1.debian.tar.xz 5.8 KiB 6666f9e0a93979183502b81b0c818ae66b499d1ee87bdc85f498a1b6d725720b

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Binary packages built by this source

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).

r-bioc-sva-dbgsym: debug symbols for r-bioc-sva