recipe bioconductor-sigcheck

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

(downloads) docker_bioconductor-sigcheck


2.16.0-1, 2.14.0-0

Required By


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<tag>

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