Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets

Background: Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test). The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets). Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first null hypothesis) is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such methods may erroneously associate the biology of a particular geneset with cancer prognosis. Re...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Esteban Czwan, Benedikt Brors, David Kipling
• Keywords: BMCBI 2010, Current Methods, Null Hypothesis, Random Genesets