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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.
SUBMITTER: De Meulder B
PROVIDER: S-EPMC5975674 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature
De Meulder Bertrand B Lefaudeux Diane D Bansal Aruna T AT Mazein Alexander A Chaiboonchoe Amphun A Ahmed Hassan H Balaur Irina I Saqi Mansoor M Pellet Johann J Ballereau Stéphane S Lemonnier Nathanaël N Sun Kai K Pandis Ioannis I Yang Xian X Batuwitage Manohara M Kretsos Kosmas K van Eyll Jonathan J Bedding Alun A Davison Timothy T Dodson Paul P Larminie Christopher C Postle Anthony A Corfield Julie J Djukanovic Ratko R Chung Kian Fan KF Adcock Ian M IM Guo Yi-Ke YK Sterk Peter J PJ Manta Alexander A Rowe Anthony A Baribaud Frédéric F Auffray Charles C
BMC systems biology 20180529 1
<h4>Background</h4>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.<h4>Methods</h4>The framework is divided into four major steps: dataset sub ...[more]