Text-based over-representation analysis of microarray gene lists with annotation bias.

ABSTRACT: A major challenge in microarray data analysis is the functional interpretation of gene lists. A common approach to address this is over-representation analysis (ORA), which uses the hypergeometric test (or its variants) to evaluate whether a particular functionally defined group of genes is represented more than expected by chance within a gene list. Existing applications of ORA have been largely limited to pre-defined terminologies such as GO and KEGG. We report our explorations of whether ORA can be applied to a wider mining of free-text. We found that a hitherto underappreciated feature of experimentally derived gene lists is that the constituents have substantially more annotation associated with them, as they have been researched upon for a longer period of time. This bias, a result o

SUBMITTER: Leong HS 

PROVIDER: S-EPMC2699530 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature