SigCheck

Check a gene signature's classification performance against random signatures, permuted data, and known signatures.

Bioconductor version: 3.0

While gene signatures are frequently used to classify data (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 partly on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its classification performance (using the MLInterfaces package) against a) random gene signatures of the same length; b) known, (related and unrelated) gene signatures; and c) permuted data.

Author: Justin Norden <jn333 at cam.ac.uk> and Rory Stark <rory.stark at cruk.cam.ac.uk>

Maintainer: Rory Stark <rory.stark at cruk.cam.ac.uk>

Installation

To install this package, start R and enter:

source("http://bioconductor.org/biocLite.R")
biocLite("SigCheck")

Documentation

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

browseVignettes("SigCheck")

Details

Package Archives

Follow Installation instructions to use this package in your R session.