Bi-clustering of metabolic data using matrix factorization tools

Gu, Q. and Veselkov, K. (2018) Bi-clustering of metabolic data using matrix factorization tools. Methods, 151, pp. 12-20. (doi: 10.1016/j.ymeth.2018.02.004) (PMID:29438828) (PMCID:PMC6297113)

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Metabolic phenotyping technologies based on Nuclear Magnetic Spectroscopy (NMR) and Mass Spectrometry (MS) generate vast amounts of unrefined data from biological samples. Clustering strategies are frequently employed to provide insight into patterns of relationships between samples and metabolites. Here, we propose the use of a non-negative matrix factorization driven bi-clustering strategy for metabolic phenotyping data in order to discover subsets of interrelated metabolites that exhibit similar behaviour across subsets of samples. The proposed strategy incorporates bi-cross validation and statistical segmentation techniques to automatically determine the number and structure of bi-clusters. This alternative approach is in contrast to the widely used conventional clustering approaches that incorporate all molecular peaks for clustering in metabolic studies and require a priori specification of the number of clusters. We perform the comparative analysis of the proposed strategy with other bi-clustering approaches, which were developed in the context of genomics and transcriptomics research. We demonstrate the superior performance of the proposed bi-clustering strategy on both simulated (NMR) and real (MS) bacterial metabolic data.

Item Type:Articles
Additional Information:We acknowledge the financial support for bioinformatics developments as part of MRC (MC_UU_12014/12), BBSRC (BB/ L020858/1) and EU-METASPACE (34402) projects; KV acknowledges Waters corporation for funding and support throughout this study.
Glasgow Author(s) Enlighten ID:Gu, Dr Quan
Authors: Gu, Q., and Veselkov, K.
College/School:College of Medical Veterinary and Life Sciences > School of Infection & Immunity
College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research
Journal Name:Methods
ISSN (Online):1046-2023
Published Online:10 February 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Methods 151: 12-20
Publisher Policy:Reproduced under a Creative Commons License

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