integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth

摘要: Background: The integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection lists candidates stemming from different types data that have been generated imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing chance missing potentially important candidates. Methods: To better facilitate unbiased collected diverse platforms samples, we propose desirability function framework for identifying with strong evidence across targets follow-up functional analysis. Our is targeted towards disease systems sparse, so tested it one pathology: spontaneous preterm birth (sPTB). Results: We developed software integRATE, which uses functions rank both within studies, well-supported according cumulative weight biological rather than based imposition key variables. Integrating 10 sPTB studies identified pathways previously suspected be well novel never before linked this syndrome. integRATE available an R package GitHub (https://github.com/haleyeidem/integRATE). Conclusions: Desirability-based solution applicable research areas where especially allowing prioritization can used inform more downstream analyses.