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