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Bioinformatics Advance Access published online on February 26, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth158
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received October 9, 2003
Revised December 23, 2003
Accepted January 7, 2004

Article

Robust PCA and classification in biosciences

Mia Huber 1* Sanne Engelen 1

1 Department of Mathematics, Katholieke Universiteit Leuven, W. De Croylaan 54, B-3001 Leuven, Belgium

* To whom correspondence should be addressed. E-mail: Mia.Hubert{at}wis.kuleuven.ac.be.


  Abstract

Motivation: Principal components analysis (PCA) is a very popular dimension reduction technique which is widely used as a first step in the analysis of high-dimensional microarray data. However, the classical approach which is based on the mean and the sample covariance matrix of the data is very sensitive to outliers. Also classification methods based on this covariance matrix give bad results in the presence of outlying measurements.

Results: First we propose a robust PCA method for high-dimensional data. It combines projection-pursuit ideas with robust estimation of low-dimensional data. We also propose a diagnostic plot to display and classify the outliers. This ROBPCA method is applied to several biochemical data sets. In one example, we also apply a robust discriminant method on the scores obtained with ROBPCA. We show that this combination of robust methods leads to better classifications than classical PCA and quadratic discriminant analysis.

Availability: All the programs are part of the Matlab Toolbox for Robust Calibration, available at http://www.wis.kuleuven.ac.be/stat/robust.html.


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