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Bioinformatics Advance Access published online on December 20, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl632
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 23, 2006
Revised December 7, 2006
Accepted December 8, 2006

Article

Clustering Threshold Gradient Descent Regularization: with applications to microarray studies

Shuangge Ma 1 * and Jian Huang 2

1 Department of Epidemiology and Public Health, Yale University, New Haven, CT, USA
2 Department of Statistics, University of Iowa, Iowa City, IA, USA; Department of Actuarial Science, University of Iowa, Iowa City, IA, USA

* To whom correspondence should be addressed.
Shuangge Ma, E-mail: shuangge.ma{at}yale.edu


  Abstract

Motivation: An important goal of microarray studies is to discover genes that are associated with clinical outcomes such as disease status and patient survival. While a typical experiment surveys gene expressions on a global scale, there may be only a small number of genes that have significant influence on a clinical outcome. Moreover, expression data have cluster structures and the genes within a cluster have correlated expressions and coordinated functions, but the effects of individual genes in the same cluster may be different. Accordingly, we seek to build statistical models with the following properties. First, the model is sparse in the sense that only a subset of the parameter vector is non-zero. Second, the cluster structures of gene expressions are properly accounted for.

Results: For gene expression data without pathway information, we divide genes into clusters are using commonly used methods such as K-means or hierarchical approaches. The optimal number of clusters is determined using the Gap statistic. We propose a Clustering Threshold Gradient Descent Regularization (CTGDR) method, for simultaneous cluster selection and within cluster gene selection. We apply this method to binary classification and censored survival analysis. Compared to the standard TGDR and other regularization methods, the CTGDR takes into account the cluster structure and carries out feature selection at both the cluster level and withincluster gene level. We demonstrate the CTGDR on two studies of cancer classification and two studies correlating survival of lymphoma patients with microarray expressions.

Availability: R code is available upon request.

Associate Editor: Satoru Miyano
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 BiometrikaHome page
J. Huang, S. Ma, H. Xie, and C.-H. Zhang
A group bridge approach for variable selection
Biometrika, June 1, 2009; 96(2): 339 - 355.
[Abstract] [PDF]


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