Multiple Tests of Association with Biological Annotation Metadata

• Activity Number: 126
• Type: Invited
• Date/Time: Monday, July 30, 2007 : 10:30 AM to 12:20 PM
• Sponsor: General Methodology
• Author(s): Sunduz Keles*+ and Sandrine Dudoit and Mark J. van der Laan
• Companies: University of Wisconsin-Madison and University of California, Berkeley and University of California, Berkeley
• Keywords: Multiple testing ; Type-I error rates ; Resampling-based approaches ; Gene set enrichment analysis ; Biological meta data ; Gene ontology

Abstract

We propose a general and formal statistical framework for the multiple tests of associations between known fixed features of a genome and unknown parameters of the distribution of variable features of this genome in a population of interest. The known fixed gene-annotation profiles may concern Gene Ontology (GO) annotation, regulation by particular transcription factors, and pathway membership. The gene-parameter profiles may be, for example, regression coefficients relating genome-wide transcript levels or DNA copy numbers to clinical outcomes. A rigorous formulation of the inference question allows us to apply the multiple testing methodology developed in Dudoit and van der Laan (2006) to control a broad class of Type I error rates, in testing problems with general data generating distributions. The proposed methods are illustrated using the ALL dataset of Chiaretti et al. (2004).