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