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

Bioconductor version: Release (3.19)

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.

Author: Rory Stark <rory.stark at> and Justin Norden

Maintainer: Rory Stark <rory.stark at>

Citation (from within R, enter citation("SigCheck")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

Checking gene expression signatures against random and known signatures with SigCheck PDF R Script
Reference Manual PDF


biocViews Classification, GeneExpression, GeneSetEnrichment, Software
Version 2.36.0
In Bioconductor since BioC 3.0 (R-3.1) (10 years)
License Artistic-2.0
Depends R (>= 4.0.0), MLInterfaces, Biobase, e1071, BiocParallel, survival
Imports graphics, stats, utils, methods
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Suggests BiocStyle, breastCancerNKI, qusage
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Source Package SigCheck_2.36.0.tar.gz
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macOS Binary (x86_64) SigCheck_2.36.0.tgz
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