Text-based over-representation analysis of microarray gene lists with annotation bias.
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 of patterns of research activity within the biomedical community, is a major problem for classical hypergeometric test-based ORA approaches, which cannot account for such bias. We have therefore developed three approaches to overcome this bias, and demonstrate their usability in a wide range of published datasets covering different species. A comparison with existing tools that use GO terms suggests that mining PubMed abstracts can reveal additional biological insight that may not be possible by mining pre-defined ontologies alone.
Leong
Hui Sun
HS
Department of Pathology, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.
Kipling
David
D
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D005796:Genes; D016247:Information Storage and Retrieval / Q000379:methods*; D007372:Interferons / Q000502:physiology; D020411:Oligonucleotide Array Sequence Analysis; D039781:PubMed; D012984:Software; D018875:Vocabulary, Controlled
D007372:Interferons