Bioinformatics Advance Access published online on November 5, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti114
Bioinformatics © Oxford University Press 2004; all rights reserved
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1 CNRS/LMC-IMAG, BP 53, 38041 Grenoble cedex 9, France
* To whom correspondence should be addressed.
Motivation: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to the number p of genes per sample (usually in thousands). It is known that standard statistical methods in classification are efficient (i.e. in the present case, yield successful classifiers) particularly when n is (far) larger than p. This naturally calls for the use of a dimension reduction procedure together with the classification one. Results: In this paper, the question of classification in such a high dimensional setting is addressed. We view the classification problem as a regression one with few observations and many predictor variables. We propose a new method combining Partial Least Squares (PLS) and Ridge penalized logistic regression. We review the existing methods based on PLS and/or penalized likelihood techniques, outline their interest in some cases and theoretically explain their sometimes poor behavior. Our procedure is compared with these other classifiers. The predictive performance of the resulting classification rule is illustrated on three data sets: Leukemia, Colon and Prostate. Availability: Software that implements the procedures and data source on which this paper focuses are freely available at http://www-lmc.imag.fr/SMS/membres/Gersende_Fort,Sophie_Lambert.html.
Revised October 5, 2004
Accepted October 22, 2004
Article
Classification using Partial Least Squares with penalized logistic regression
Gersende Fort, E-mail: Gersende.Fort{at}imag.fr
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