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

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 analys

SUBMITTER: De Meulder B 

PROVIDER: S-EPMC5975674 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature