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Open Access Highly Accessed Research article

Super-sparse principal component analyses for high-throughput genomic data

Donghwan Lee1, Woojoo Lee2, Youngjo Lee1 and Yudi Pawitan2*

Author Affiliations

1 Department of Statistics, Seoul National University, Seoul, South Korea

2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

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BMC Bioinformatics 2010, 11:296  doi:10.1186/1471-2105-11-296

Published: 2 June 2010

Abstract

Background

Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are typically nonzero. These nonzero values also reflect poor estimation of the true vector loadings; for example, for gene expression data, biologically we expect only a portion of the genes to be expressed in any tissue, and an even smaller fraction to be involved in a particular process. Sparse PCA methods have recently been introduced for reducing the number of nonzero coefficients, but these existing methods are not satisfactory for high-dimensional data applications because they still give too many nonzero coefficients.

Results

Here we propose a new PCA method that uses two innovations to produce an extremely sparse loading vector: (i) a random-effect model on the loadings that leads to an unbounded penalty at the origin and (ii) shrinkage of the singular values obtained from the singular value decomposition of the data matrix. We develop a stable computing algorithm by modifying nonlinear iterative partial least square (NIPALS) algorithm, and illustrate the method with an analysis of the NCI cancer dataset that contains 21,225 genes.

Conclusions

The new method has better performance than several existing methods, particularly in the estimation of the loading vectors.