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

作者：De Meulder Bertrand^*; Lefaudeux Diane; Bansal Aruna T; Mazein Alexander; Chaiboonchoe Amphun; Ahmed Hassan; Balaur Irina; Saqi Mansoor; Pellet Johann; Ballereau Stephane; Lemonnier Nathanael; Sun Kai; Pandis Ioannis; Yang Xian; Batuwitage Manohara; Kretsos Kosmas; van Eyll Jonathan; Bedding Alun; Davison Timothy; Dodson Paul; Larminie Christopher; Postle Anthony; Corfield Julie; Djukanovic Ratko; Chung Kian Fan; Adcock Ian M; Guo Yi Ke; Sterk Peter J

来源：BMC Systems Biology, 2018, 12(1): 60.

DOI：10.1186/s12918-018-0556-z

• Molecular signatures
• Omics data
• Stratification
• Systems medicine

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.

• 出版日期2018-5-29