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パッケージ: r-bioc-qusage (2.28.0-1)

r-bioc-qusage に関するリンク

r-bioc-qusage

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r-bioc-qusage ソースパッケージをダウンロード:

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Original Maintainers:

  • Debian R Packages Maintainers
  • Steffen Moeller

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類似のパッケージ:

qusage: Quantitative Set Analysis for Gene Expression

This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu)

その他の r-bioc-qusage 関連パッケージ

  • 依存
  • 推奨
  • 提案
  • dep: r-api-4.0
    以下のパッケージによって提供される仮想パッケージです: r-base-core
  • dep: r-api-bioc-3.14
    以下のパッケージによって提供される仮想パッケージです: r-bioc-biocgenerics
  • dep: r-base-core (>= 4.1.2-1ubuntu1)
    GNU R core of statistical computation and graphics system
  • dep: r-bioc-biobase
    base functions for Bioconductor
  • dep: r-bioc-limma (>= 3.14)
    linear models for microarray data
  • dep: r-cran-emmeans
    GNU R estimated marginal means, aka least-squares means
  • dep: r-cran-fftw
    GNU R fast FFT and DCT Based on the FFTW Library
  • dep: r-cran-nlme
    GNU R package for (non-)linear mixed effects models

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