This article is part of the supplement: IEEE 7th International Conference on Bioinformatics and Bioengineering at Harvard Medical School

Research

Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis

Guo-Zheng Li^1,2, Hua-Long Bu², Mary Q Yang³, Xue-Qiang Zeng² and Jack Y Yang^4*

• * Corresponding author: Jack Y Yang yang@hadron.mgh.harvard.edu

Author Affiliations

¹ Department of Control Science & Engineering, Tongji University, Shanghai 201804, PR China

² School of Computer Science & Engineering, Shanghai University, Shanghai 200072, PR China

³ National Human Genome Research Institute National Institutes of Health (NIH) U.S., Department of Health and Human Services Bethesda, MD 20852, USA

⁴ Harvard Medical School, Harvard University, Cambridge, MA 02140-0888, USA

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BMC Genomics 2008, 9(Suppl 2):S24 doi:10.1186/1471-2164-9-S2-S24

Published: 16 September 2008

Abstract

Background

Dimension reduction is a critical issue in the analysis of microarray data, because the high dimensionality of gene expression microarray data set hurts generalization performance of classifiers. It consists of two types of methods, i.e. feature selection and feature extraction. Principle component analysis (PCA) and partial least squares (PLS) are two frequently used feature extraction methods, and in the previous works, the top several components of PCA or PLS are selected for modeling according to the descending order of eigenvalues. While in this paper, we prove that not all the top features are useful, but features should be selected from all the components by feature selection methods.

Results

We demonstrate a framework for selecting feature subsets from all the newly extracted components, leading to reduced classification error rates on the gene expression microarray data. Here we have considered both an unsupervised method PCA and a supervised method PLS for extracting new components, genetic algorithms for feature selection, and support vector machines and k nearest neighbor for classification. Experimental results illustrate that our proposed framework is effective to select feature subsets and to reduce classification error rates.

Conclusion

Not only the top features newly extracted by PCA or PLS are important, therefore, feature selection should be performed to select subsets from new features to improve generalization performance of classifiers.