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Nucleic Acids Research 2005 33(1):56-65; doi:10.1093/nar/gki144
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Published online 7 January 2005

© 2005, the authors Nucleic Acids Research, Vol. 33 No. 1 © Oxford University Press 2005; all rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use permissions, please contact journals.permissions{at}oupjournals.org.

Article

Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data

Yongxi Tan, Leming Shi1, Weida Tong1 and Charles Wang*

Department of Medicine, Cedars-Sinai Medical Center, David Geffen School of Medicine UCLA, Los Angeles, CA 90048, USA 1 Center for Toxicoinformatics, Division of Systems Toxicology, National Center for Toxicological Research, FDA Jefferson, AR 72079, USA

*To whom correspondence should be addressed. Tel: +1 310 4237363; Fax: +1 310 4237452; Email: charles.wang{at}cshs.org

Received August 13, 2004. Revised November 9, 2004. Accepted December 3, 2004.

DNA microarray technology provides a promising approach to the diagnosis and prognosis of tumors on a genome-wide scale by monitoring the expression levels of thousands of genes simultaneously. One problem arising from the use of microarray data is the difficulty to analyze the high-dimensional gene expression data, typically with thousands of variables (genes) and much fewer observations (samples), in which severe collinearity is often observed. This makes it difficult to apply directly the classical statistical methods to investigate microarray data. In this paper, total principal component regression (TPCR) was proposed to classify human tumors by extracting the latent variable structure underlying microarray data from the augmented subspace of both independent variables and dependent variables. One of the salient features of our method is that it takes into account not only the latent variable structure but also the errors in the microarray gene expression profiles (independent variables). The prediction performance of TPCR was evaluated by both leave-one-out and leave-half-out cross-validation using four well-known microarray datasets. The stabilities and reliabilities of the classification models were further assessed by re-randomization and permutation studies. A fast kernel algorithm was applied to decrease the computation time dramatically. (MATLAB source code is available upon request.)


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Y. Tan, L. Shi, S. M. Hussain, J. Xu, W. Tong, J. M. Frazier, and C. Wang
Integrating time-course microarray gene expression profiles with cytotoxicity for identification of biomarkers in primary rat hepatocytes exposed to cadmium
Bioinformatics, January 1, 2006; 22(1): 77 - 87.
[Abstract] [Full Text] [PDF]


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