Integrative Functional Analysis Improves Information Retrieval In Breast Cancer

Gene expression analysis does not end in a list of differentially expressed (DE) genes, but requires a comprehensive functional analysis (FA) of the underlying molecular mechanisms. Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology (GO) are the most used FA approaches. Several statistical methods have been developed and compared in terms of computational efficiency and/or appropriateness. However, none of them were evaluated from a biological point of view or in terms of consistency on information retrieval. In this context, questions regarding "are methods comparable?", "is one of them preferable to the others?", "how sensitive are they to different parameterizations?" All of them are crucial questions to face prior 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 SEA under GO. Several experiments comparing breast cancer subtypes with known different outcome (i.e. Basal-Like vs. Luminal A) were analyzed. We show that GSEA could lead to very different results according to the used statistic, model and parameters. We also show that GSEA and SEA results are fairly overlapped, indeed they complement each other. Also an integrative framework is proposed to provide complementary and a stable enrichment information according to the analyzed datasets.