SigCheck: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata
Version 2.8.0

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 details

AuthorRory Stark <[email protected]> and Justin Norden
Bioconductor views Classification GeneExpression GeneSetEnrichment
MaintainerRory Stark <[email protected]>
LicenseArtistic-2.0
Version2.8.0
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("SigCheck")

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SigCheck documentation built on May 31, 2017, 2:18 p.m.