Bioinformatics Advance Access originally published online on June 16, 2004
Bioinformatics 2004 20(17):3021-3033; doi:10.1093/bioinformatics/bth354
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Bioinformatics vol. 20 issue 17 © Oxford University Press 2004; all rights reserved.
Biologically valid linear factor models of gene expression
1 Bioinformatics Research Centre, Department of Computing Science and 2 Molecular Plant Sciences Group, Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK
Received on April 23, 2004; revised on May 19, 2004; accepted on May 31, 2004
Advance Access Publication June 16, 2004
Motivation: The identification of physiological processes underlying and generating the expression pattern observed in microarray experiments is a major challenge. Principal component analysis (PCA) is a linear multivariate statistical method that is regularly employed for that purpose as it provides a reduced-dimensional representation for subsequent study of possible biological processes responding to the particular experimental conditions. Making explicit the data assumptions underlying PCA highlights their lack of biological validity thus making biological interpretation of the principal components problematic. A microarray data representation which enables clear biological interpretation is a desirable analysis tool.
Results: We address this issue by employing the probabilistic interpretation of PCA and proposing alternative linear factor models which are based on refined biological assumptions. A practical study on two well-understood microarray datasets highlights the weakness of PCA and the greater biological interpretability of the linear models we have developed.
Availability: The model estimation routines are currently implemented as Matlab routines and these, as well as data and results reported, are available from the following URL: http://www.dcs.gla.ac.uk/~girolami/lfm/index.html
Contact: girolami{at}dcs.gla.ac.uk
* To whom correspondence should be addressed.
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