Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology

Lee, C.M., Mudaliar, M.A.V. , Haggart, D.R., Wolf, C.R., Miele, G., Vass, J.K., Higham, D.J. and Crowther, D. (2012) Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology. PLoS ONE, 7(12), e48238. (doi: 10.1371/journal.pone.0048238) (PMID:23272042) (PMCID:PMC3522745)

[img]
Preview
Text
75131.pdf - Published Version
Available under License Creative Commons Attribution.

1MB

Abstract

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Mudaliar, Dr Manikhandan
Authors: Lee, C.M., Mudaliar, M.A.V., Haggart, D.R., Wolf, C.R., Miele, G., Vass, J.K., Higham, D.J., and Crowther, D.
College/School:College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
Journal Name:PLoS ONE
Publisher:Public Library of Science
ISSN:1932-6203
ISSN (Online):1932-6203
Published Online:14 December 2012
Copyright Holders:Copyright © 2012 The Authors
First Published:First published in PLoS ONE 7(12):e48238
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record