recipe bioconductor-sigcheck

Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata

Homepage

https://bioconductor.org/packages/3.14/bioc/html/SigCheck.html

License

Artistic-2.0

Recipe

/bioconductor-sigcheck/meta.yaml

While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata.

package bioconductor-sigcheck

Versions

2.26.0-0,  2.24.0-0,  2.22.0-1,  2.22.0-0,  2.20.0-0,  2.18.0-0,  2.16.0-1,  2.14.0-0

Depends

• bioconductor-biobase >=2.54.0,<2.55.0

• bioconductor-biocparallel >=1.28.0,<1.29.0

• bioconductor-mlinterfaces >=1.74.0,<1.75.0

• r-base >=4.1,<4.2.0a0

• r-e1071

• r-survival

Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-sigcheck

and update with:

conda update bioconductor-sigcheck

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-sigcheck:<tag>

(see bioconductor-sigcheck/tags for valid values for <tag>)

Download stats

Link to this page

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-sigcheck/README.html)