Bioinformatics Vol. 18 no. 12 2002
Pages 1617-1624
© 2002 Oxford University Press
A decomposition model to track gene expression signatures: preview on observer-independent classification of ovarian cancer
1 Reproductive Molecular Research Group,
Department of Pathology and Department of Obstetrics and Gynaecology,
University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK
2 Cavendish Astrophysics Group, Cavendish Laboratory,
University of Cambridge, Madingley Road, Cambridge, CB3 0HE, UK
Received on February 8, 2002
; revised on May 8, 2002
; accepted on June 6, 2002
Motivation: A number of algorithms and analytical models have been employed to reduce the multidimensional complexity of DNA array data and attempt to extract some meaningful interpretation of the results. These include clustering, principal components analysis, self-organizing maps, and support vector machine analysis. Each method assumes an implicit model for the data, many of which separate genes into distinct clusters defined by similar expression profiles in the samples tested. A point of concern is that many genes may be involved in a number of distinct behaviours, and should therefore be modelled to fit into as many separate clusters as detected in the multidimensional gene expression space. The analysis of gene expression data using a decomposition model that is independent of the observer involved would be highly beneficial to improve standard and reproducible classification of clinical and research samples.
Results: We present a variational independent component analysis (ICA) method for reducing high dimensional DNA array data to a smaller set of latent variables, each associated with a gene signature. We present the results of applying the method to data from an ovarian cancer study, revealing a number of tissue type-specific and tissue type-independent gene signatures present in varying amounts among the samples surveyed. The observer independent results of such molecular analysis of biological samples could help identify patients who would benefit from different treatment strategies. We further explore the application of the model to similar high-throughput studies.
Availability: Supporting details of the decomposition model can be found at http://www.inference.phy.cam.ac.uk/mackay/abstracts/icagenes.html and the ovarian cancer study data can be found at http://www.path.cam.ac.uk/~angio/publications/martoglioetal2002/ovcaica.html.
Contact: amm53{at}cam.ac.uk; mackay{at}mrao.cam.ac.uk
* To whom correspondence should be addressed.
These authors contributed equally to this
work.
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