Pakiet: r-bioc-sva (3.50.0-1)

[ Pakiet źródłowy: 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).

Inne pakiety związane z r-bioc-sva

• dep: r-api-4.0

  pakiet wirtualny udostępniany przez r-base-core

• dep: r-api-bioc-3.18

  pakiet wirtualny udostępniany przez r-bioc-biocgenerics

• dep: r-bioc-biocparallel

  BioConductor facilities for parallel evaluation

• dep: r-bioc-edger

  Empirical analysis of digital gene expression data in R

• dep: r-bioc-genefilter

  methods for filtering genes from microarray experiments

• dep: r-bioc-limma

  linear models for microarray data

• dep: r-cran-matrixstats

  GNU R methods that apply to rows and columns of a matrix

• dep: r-cran-mgcv

  GNU R package for multiple parameter smoothing estimation

• sug: r-bioc-biocstyle

  standard styles for vignettes and other Bioconductor documents

• sug: r-bioc-bladderbatch

  GNU R bladder gene expression data illustrating batch effects

• sug: r-cran-testthat

  GNU R testsuite

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