A computational framework for complex disease stratification from multiple large-scale datasets
Author(s)
Type
Journal Article
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.
Date Issued
2018-05-29
Date Acceptance
2018-02-21
Citation
BMC Systems Biology, 2018, 12 (1)
ISSN
1752-0509
Publisher
BioMed Central
Journal / Book Title
BMC Systems Biology
Volume
12
Issue
1
Copyright Statement
© The Author(s). 2018.
This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (
http://creativecommons.org/licenses/by/4.0/
), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(
http://creativecommons.org/publicdomain/zero/1.0/
) applies to the data made available in this article, unless otherwise stated.
This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (
http://creativecommons.org/licenses/by/4.0/
), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(
http://creativecommons.org/publicdomain/zero/1.0/
) applies to the data made available in this article, unless otherwise stated.
Sponsor
Commission of the European Communities
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/29843806
PII: 10.1186/s12918-018-0556-z
Grant Number
115010
Subjects
Molecular signatures
Stratification
Systems medicine
‘Omics data
Publication Status
Published
Coverage Spatial
England
Article Number
ARTN 60