title: Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology.
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.
authors.affiliation: Department of Mathematics and Statistics, University of Strathclyde, Glasgow, United Kingdom.
authors.forename: Clare M
authors.initials: CM
authors.lastname: Lee
authors.forename: Manikhandan A V
authors.initials: MA
authors.lastname: Mudaliar
authors.forename: D R
authors.initials: DR
authors.lastname: Haggart
authors.forename: C Roland
authors.initials: CR
authors.lastname: Wolf
authors.forename: Gino
authors.initials: G
authors.lastname: Miele
authors.forename: J Keith
authors.initials: JK
authors.lastname: Vass
authors.forename: Desmond J
authors.initials: DJ
authors.lastname: Higham
authors.forename: Daniel
authors.initials: D
authors.lastname: Crowther
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000187:drug effects*; D008099:Liver / Q000378:metabolism; D051379:Mice; D008810:Mice, Inbred C57BL; D015233:Models, Statistical; D005810:Multigene Family; D009132:Muscles / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D047492:Peroxisome Proliferator-Activated Receptors / Q000819:agonists; D014116:Toxicology / Q000379:methods*
chemical_list: D047492:Peroxisome Proliferator-Activated Receptors