A computational framework for complex disease stratification from multiple large-scale datasets

Bertrand De Meulder, Diane Lefaudeux, Aruna T. Bansal, Alexander Mazein, Amphun Chaiboonchoe, Hassan Ahmed, Irina Balaur, Mansoor Saqi, Johann Pellet, Stéphane Ballereau, Nathanaël Lemonnier, Kai Sun, Ioannis Pandis, Xian Yang, Manohara Batuwitage, Kosmas Kretsos, Jonathan van Eyll, Alun Bedding, Timothy Davison, Paul Dodson, Christopher Larminie, Anthony Postle, Julie Corfield, Ratko Djukanovic, Kian Fan Chung, Ian M. Adcock, Yi-Ke Guo, Peter J. Sterk, Alexander Manta, Anthony Rowe, Frédéric Baribaud, Charles Auffray, the U-BIOPRED Study Group and the eTRIKS Consortium

Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.