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Bioinformatics Advance Access originally published online on September 5, 2006
Bioinformatics 2006 22(21):2667-2673; doi:10.1093/bioinformatics/btl463
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Targeted projection pursuit for visualizing gene expression data classifications

Joe Faith 1,*, Robert Mintram 2 and Maia Angelova 1

1 Northumbria University Newcastle, UK
2 Bournemouth University Bournemouth, UK

*To whom correspondence should be addressed.

We present a novel method for finding low-dimensional views of high-dimensional data: Targeted Projection Pursuit. The method proceeds by finding projections of the data that best approximate a target view. Two versions of the method are introduced; one version based on Procrustes analysis and one based on an artificial neural network. These versions are capable of finding orthogonal or non-orthogonal projections, respectively. The method is quantitatively and qualitatively compared with other dimension reduction techniques. It is shown to find 2D views that display the classification of cancers from gene expression data with a visual separation equal to, or better than, existing dimension reduction techniques.

Availability: source code, additional diagrams, and original data are available from http://computing.unn.ac.uk/staff/CGJF1/tpp/bioinf.html

Contact: joe.faith{at}unn.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Received on May 15, 2006; revised on August 24, 2006; accepted on August 25, 2006

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