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Bioinformatics Advance Access published online on July 14, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl384
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© 2006 The Author(s)
Received March 22, 2006
Revised June 29, 2006
Accepted July 6, 2006

Article

CART variance stabilization and regularization for high throughput genomic data

Ariadni Papana 1 and Hemant Ishwaran 2 *

1 Dept. of Statistics, Case University, 10900 Euclid Avenue, Cleveland OH 44106
2 Dept. of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue, Cleveland OH 44195

* To whom correspondence should be addressed.
Hemant Ishwaran, E-mail: hemant.ishwaran{at}gmail.com


  Abstract

Motivation: mRNA expression data obtained from high throughput DNA microarrays exhibit strong departures from homogeneity of variances. Often a complex relationship between mean expression value and variance is seen. Variance stabilization of such data is crucial for many types of statistical analyses, while regularization of variances (pooling of information) can greatly improve overall accuracy of test statistics.

Results: A Classification and Regression Tree (CART) procedure is introduced for variance stabilization as well as regularization. The CART procedure adaptively clusters genes by variances. Using both local and cluster wide information leads to improved estimation of population variances which improves test statistics. Whereas making use of cluster wide information allows for variance stabilization of data.

Availability: Sufficient details for our CART procedure are given so that the interested reader can program the method for themselves. The algorithm is also accessible within the Java software package BAMarrayTM, which is freely available to non-commercial users at www.bamarray.com.

Associate Editor: Joaquin Dopazo
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