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

BMC Systems Biology
Bertrand De MeulderU-BIOPRED Study Group and the eTRIKS Consortium

Abstract

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.

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Citations

Jan 31, 2019·Genes·Bilal MirzaPeipei Ping
Jul 6, 2019·The European Respiratory Journal·Adnan AzimPeter Howarth
Apr 21, 2019·Allergy·Olga IvanovaAnke H Maitland-van der Zee

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Software Mentioned

DIABLO
TCGA
LGOCV
DA
MixOmics
R
FireBrowse TCGA
AlzPathway
iCluster
ComBat

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