An equivalence testing approach for the integrative analysis of multiple gene lists
Creators: name: Sanchez-Pla, Alex
name: Salicrú, Miquel
name: Ocaña, Jordi
Publisher: name: F1000Research
Year: 2017

description: The analysis of gene lists (or other features such as proteins, microRNAs etc.) has been a very active field since the appearance of omics data analysis. Its main application has been in the development of many distinct but related methods and tools for biological significance analysis -Over Representation Analysis and Gene Set Enrichment Analysis (3) being the best known - but also the development of gene sets and signatures databases that have been applied for example for classification or prognostic purposes (1, 2).<br><br>Curiously one topic that has received much less attention than obtaining or applying gene lists is their comparison, not in terms of gene ranking, but from the point of view of their biological meaning. Comparing gene lists seems a very relevant problem, especially in the post-genomics age, where multiple datasets from the same or different type, are available for study. This can be used for example, in data fusion problems to decide if some datasets may be merged, in data mining of gene signatures to decide if certain gene sets are equivalent or in a meta-analysis context where several studies can be compared through the gene lists they have produced.<br><br>Although a few methods have been developed for the comparison between gene lists, the <i>goProfiles</i> approach (4, 5) is, to our knowledge, one of the few that is being used for that purpose relying on the functional representation of the gene lists (alternative methods based on comparing ranked gene lists have different purpose and are not considered here). Very briefly this approach consists of taking lists of genes and projecting them into predefined levels or slices of the Gene Ontology, in such a way (called “expanded functional profiles”) that a multinomial model can be used for estimation and testing. In many analyses the main interest is in establishing -not in rejecting- the similarity between two lists so that, instead of the classical hypothesis tests setting, designed to reject a null hypothesis of equality we derived an equivalence method which uses a distance-based approach and the confidence interval inclusion principle. Equivalence is declared if a one-sided confidence inter­val for the distance between two proﬁles is below a pre-established equivalence limit. In this work, we show how the equivalence test developed for one pair of lists can be naturally extended to establish the equivalence of any number of gene lists by taking an iterative approach that combines a bottom-up approach to determining the most to least equivalent gene lists while adjusting for multiple testing. The applicability of the method will be demonstrated on two typical situations for this approach. By one side it will be applied to the comparison of two groups of gene lists, one made by Cancer-related gene lists (http://www.bushmanlab.org/links/genelists) and the other by pathogenesis-based transcripts sets (http://atagc.med.ualberta.ca/Research/GeneLists/Pages/default.aspx). The comparison not only shows how the lists tend to be more similar according to their origin but it also helps to classify the lists of each group by their similarity. The Bioconductor R package goProfiles(5) has been extended to include these new capabilities. It will be available with Bioconductor 3.6 or (in case of any issue) directly from github (\url{https:/github.com/alexsanchezpla/goProfiles}).<br><br>[1] Beck, Andrew H., Nicholas W. Knoblauch, Marco M. Hefti, et al. PLOS Computational Biology 9(1): e1002875.<br>[2] Birrer, Michael J., Markus Riester, Wei Wei, et al. Journal of Clinical Oncology 32(15_suppl): 5531-5531.<br>[3] Khatri, Purvesh, Marina Sirota, and Atul J. Butte. PLOS Computational Biology 8(2): e1002375.<br>[4] Salicrú, Miquel, Jordi Ocaña, and Alex Sánchez-Pla 2011. BMC Bioinformatics 12: 401 (1-13)<br>[5] Sanchez, A, Ocana J., Salicru M. http://bioconductor.org/packages/goProfiles