Integrative Functional Analysis improves information retrieval in Breast Cancer

RODRIGUEZ, JUAN CRUZ; GONZALEZ, GERMÁN; PRATO, LAURA; FRESNO, CRISTOBAL; FERNÁNDEZ, ELMER ANDRÉS

Montevideo

Congreso; XX Iberoamerican Congress on Pattern Recognition; 2015

CIARP

Abstract. Gene expression analysis does not end in a list of differentially ex-pressed (DE) genes, but requires a comprehensive functional analysis (FA) ofthe underlying molecular mechanisms. Gene Set and Singular EnrichmentAnalysis (GSEA and SEA) over Gene Ontology (GO) are the most used FAapproaches. Several statistical methods have been developed and compared interms of computational efficiency and/or appropriateness. However, none ofthem were evaluated from a biological point of view or in terms of consistencyon information retrieval. In this context, questions regarding ?are methodscomparable??, ?is one of them preferable to the others??, ?how sensitive arethey to different parameterizations?? All of them are crucial questions to faceprior choosing a FA tool and they have not been, up to now, fully addressed.In this work we evaluate and compare the effect of different methods and parameters from an information retrieval point of view in both GSEA and SEAunder GO. Several experiments comparing breast cancer subtypes with knowndifferent outcome (i.e. Basal-Like vs. Luminal A) were analyzed. We show thatGSEA could lead to very different results according to the used statistic, modeland parameters. We also show that GSEA and SEA results are fairly over-lapped, indeed they complement each other. Also an integrative framework isproposed to provide complementary and a stable enrichment information ac-cording to the analyzed datasets.