Inferential Tests for the Intersection of Independent Gene Lists

• Activity Number: 351
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: WNAR
• Author(s): Loki Natarajan*+ and Karen Messer and Minya Pu
• Companies: University of California at San Diego and University of California at San Diego and University of California at San Diego
• Keywords: Concordance ; Gene-ranking ; Validation ; Cancer

Abstract

Curated public repositories of genomic data enable researchers to compare results across multiple studies. A common approach is to rank genes for a hypothesis of interest within each study. Then, lists of top-ranked genes within a study are compared across studies. Genes recaptured as highly ranked (usually above some threshold) in multiple studies are considered to be significant. In this talk, we develop a formal inferential strategy for this kind of list-intersection discovery test. We show how to compute a p-value associated with a `recaptured' set of genes, using a closed-form Poisson approximation to the distribution of the size of the recaptured set. We investigate operating characteristics of the test as a function of the total number of studies considered, the rank threshold within each study, and the number of studies within which a gene must be recaptured to be declared significant. We give practical guidance on designing bioinformatic list-intersection studies with adequate control of Type I error and false discovery rate, while maximizing expected sensitivity to capture true positives. We illustrate our methods using data from the curated Oncomine database.