Imperial College London
Alternate

DrKirillVeselkov

Faculty of Medicine, Department of Surgery & Cancer

Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 3899kirill.veselkov04

 
 
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Location

 

Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gu:2018:10.1016/j.ymeth.2018.02.004,
author = {Gu, Q and Veselkov, K},
doi = {10.1016/j.ymeth.2018.02.004},
journal = {Methods},
pages = {12--20},
title = {Bi-clustering of metabolic data using matrix factorization tools},
url = {http://dx.doi.org/10.1016/j.ymeth.2018.02.004},
volume = {151},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 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.
AU  - Gu,Q
AU  - Veselkov,K
DO  - 10.1016/j.ymeth.2018.02.004
EP  - 20
PY  - 2018///
SN  - 1046-2023
SP  - 12
TI  - Bi-clustering of metabolic data using matrix factorization tools
T2  - Methods
UR  - http://dx.doi.org/10.1016/j.ymeth.2018.02.004
UR  - http://hdl.handle.net/10044/1/57071
VL  - 151
ER  -
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