An equivalence approach to the integrative analysis of feature lists

Background Although a few comparison methods based on the biological meaning of gene lists have been developed, the goProfiles approach is one of the few that are being used for that purpose. It consists of projecting lists of genes into predefined levels of the Gene Ontology, in such a way that a multinomial model can be used for estimation and testing. Of particular interest is the fact that it may be used for proving equivalence (in the sense of 'enough similarity') between two lists, instead of proving differences between them, which seems conceptually better suited to the end goal of establishing similarity among gene lists. An equivalence method has been derived that uses a distance-based approach and the confidence interval inclusion principle. Equivalence is declared if the upper limit of a one-sided confidence interval for the distance between two profiles is below a pre-established equivalence limit. Results In this work, this method is extended to establish the equivalence of any number of gene lists. Additionally, an algorithm to obtain the smallest equivalence limit that would allow equivalence between two or more lists to be declared is presented. This algorithm is at the base of an iterative method of graphic visualization to represent the most to least equivalent gene lists. These methods deal adequately with the problem of adjusting for multiple testing. The applicability of these techniques is illustrated in two typical situations: (i) a collection of cancer-related gene lists, suggesting which of them are more reasonable to combine -as claimed by the authors- and (ii) a collection of pathogenesis-based transcript sets, showing which of these are more closely related. The methods developed are available in the goProfiles Bioconductor package. Conclusions The method provides a simple yet powerful and statistically well-grounded way to classify a set of genes or other feature lists by establishing their equivalence at a given equivalence threshold. The classification results can be viewed using standard visualization methods. This may be applied to a variety of problems, from deciding whether a series of datasets generating the lists can be combined to the simplification of groups of lists