Skip Navigation



Bioinformatics Advance Access published online on September 5, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl463
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
22/21/2667    most recent
btl463v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Faith, J.
Right arrow Articles by Angelova, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Faith, J.
Right arrow Articles by Angelova, M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 15, 2006
Revised August 24, 2006
Accepted August 25, 2006

Article

Targeted projection pursuit for visualising 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.
Joe Faith, E-mail: joe.faith{at}unn.ac.uk


  Abstract

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 two-dimensional 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.

Associate Editor: Chris Stoeckert
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.