套件： r-bioc-sva (3.42.0-1)
r-bioc-sva 的相關超連結
Trisquel 的資源：
下載原始碼套件 r-bioc-sva：
維護者：
Original Maintainers:
- Debian R Packages Maintainers
- Andreas Tille
外部的資源：
- 主頁 [bioconductor.org]
相似套件：
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).
其他與 r-bioc-sva 有關的套件
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- dep: r-api-4.0
- 本虛擬套件由這些套件提供： r-base-core
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- dep: r-api-bioc-3.14
- 本虛擬套件由這些套件提供： r-bioc-biocgenerics
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- dep: r-base-core (>= 4.1.2-1ubuntu1)
- GNU R core of statistical computation and graphics system
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- dep: r-bioc-biocparallel
- BioConductor facilities for parallel evaluation
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- dep: r-bioc-edger
- Empirical analysis of digital gene expression data in R
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- dep: r-bioc-genefilter
- methods for filtering genes from microarray experiments
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- dep: r-bioc-limma
- linear models for microarray data
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- dep: r-cran-matrixstats
- GNU R methods that apply to rows and columns of a matrix
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- dep: r-cran-mgcv
- GNU R package for multiple parameter smoothing estimation
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- sug: r-bioc-biocstyle
- standard styles for vignettes and other Bioconductor documents
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- sug: r-bioc-bladderbatch
- GNU R bladder gene expression data illustrating batch effects
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- sug: r-cran-testthat
- GNU R testsuite