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- Title
A computational framework for complex disease stratification from multiple large-scale datasets.
- Authors
De Meulder, Bertrand; Lefaudeux, Diane; Bansal, Aruna T.; Mazein, Alexander; Chaiboonchoe, Amphun; Ahmed, Hassan; Balaur, Irina; Saqi, Mansoor; Pellet, Johann; Ballereau, Stéphane; Lemonnier, Nathanaël; Sun, Kai; Pandis, Ioannis; Yang, Xian; Batuwitage, Manohara; Kretsos, Kosmas; van Eyll, Jonathan; Bedding, Alun; Davison, Timothy; Dodson, Paul
- Abstract
Background: 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. Methods: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. Results: 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. Conclusions: 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.
- Subjects
SOCIAL stratification; DATA integration; TRANSLATIONAL research; GENOMICS; CLINICAL trials
- Publication
BMC Systems Biology, 2018, Vol 12, Issue 1, pN.PAG
- ISSN
1752-0509
- Publication type
Article
- DOI
10.1186/s12918-018-0556-z