LICRE: unsupervised feature correlation reduction for lipidomics. (13th June 2014)

• Record Type: Journal Article
• Authors: Wong, Gerard; Chan, Jeffrey; Kingwell, Bronwyn A.; Leckie, Christopher; Meikle, Peter J.
• Is Part Of: Bioinformatics. Volume 30:Number 19(2014)
• Page Start: 2832
• Page End: 2833
• Publication Date: 2014-06-13
• Subjects: Bioinformatics -- Periodicals; Genomics -- Data processing -- Periodicals; Computational biology -- Periodicals; 572.80285
• DOI: 10.1093/bioinformatics/btu381
• Languages: English
• ISSNs: 1367-4803

Abstract

Abstract : Motivation: Recent advances in high-throughput lipid profiling by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) have made it possible to quantify hundreds of individual molecular lipid species (e.g. fatty acyls, glycerolipids, glycerophospholipids, sphingolipids) in a single experimental run for hundreds of samples. This enables the lipidome of large cohorts of subjects to be profiled to identify lipid biomarkers significantly associated with disease risk, progression and treatment response. Clinically, these lipid biomarkers can be used to construct classification models for the purpose of disease screening or diagnosis. However, the inclusion of a large number of highly correlated biomarkers within a model may reduce classification performance, unnecessarily inflate associated costs of a diagnosis or a screen and reduce the feasibility of clinical translation. An unsupervised feature reduction approach can reduce feature redundancy in lipidomic biomarkers by limiting the number of highly correlated lipids while retaining informative features to achieve good classification performance for various clinical outcomes. Good predictive models based on a reduced number of biomarkers are also more cost effective and feasible from a clinical translation perspective. Results: The application of LICRE to various lipidomic datasets in diabetes and cardiovascular disease demonstrated superior discrimination in terms of the area under the receiver operator characteristic curve while using fewer lipid markers when predicting various clinical outcomes. Availability and implementation: The MATLAB implementation of LICRE is available from http://ww2.cs.mu.oz.au/∼gwong/LICRE Contact: [e-mail] or [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.