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

Area of Science:

• Systems biology
• Multi-omics data integration
• Translational medicine

Background:

• Advancing systems biology requires robust methods for integrating diverse biological, clinical, and environmental data.
• Increasing availability of multi-omics datasets for complex diseases necessitates standardized analysis practices.
• Developing effective strategies for single and multi-omics signatures of disease states is crucial.

Purpose of the Study:

• To present a novel framework for planning and executing integrated multi-omics data analysis.
• To establish standards and best practices for analyzing complex disease data.
• To facilitate the generation of disease-specific signatures from multi-omics datasets.

Main Methods:

• The proposed framework involves four key steps: dataset subsetting, feature filtering, omics-based clustering, and biomarker identification.
• This systematic approach ensures a structured analysis of complex biological data.
• The methodology is designed for generating robust and reproducible omics signatures.

Main Results:

• Application of the framework to an ovarian cystadenocarcinoma dataset identified patient clusters with integrated multi-omics signatures.
• The analysis yielded a greater number of stable and clinically relevant patient clusters compared to previous studies.
• The framework enabled the development of predictive models for patient outcomes, demonstrating its clinical utility.

Conclusions:

• The presented framework offers a structured approach for health researchers to analyze multi-omics big data.
• It aids in hypothesis generation and interpretation of complex, large-scale biological datasets.
• Implementation of this framework supports the advancement of personalized P4 medicine.