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Bioinformatics Advance Access published online on February 10, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl034
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received September 19, 2005
Revised January 23, 2006
Accepted January 31, 2006

Article

Nonlinear tests for identifying differentially expressed genes or genetic networks

Hao Xiong 1 *

1 Department of Computer Science, Texas A&M University, 301 Harvey R. Bright Bldg, College Station, TX 77843-3112

* To whom correspondence should be addressed.
Hao Xiong, E-mail: hxiong{at}cs.tamu.edu


  Abstract

Motivation: One of the recently developed statistics for identifying differentially expressed genetic networks is Hotelling T2 statistic, which is a quadratic form of difference in linear functions of means of gene expressions between two types of tissue samples, and so their power is limited.

Results: To improve the power of test statistics, a general statistical framework for construction of nonlinear tests is presented, and two specific nonlinear test statistics that use nonlinear transformations of means are developed. Asymptotical distributions of the nonlinear test statistics under the null and alternative hypothesis are derived. It has been proved that under some conditions the power of the nonlinear test statistics is higher than that of the T2 statistic. Besides theory, to evaluate in practice the performance of the nonlinear test statistics, they are applied to two real data sets. The preliminary results demonstrate that the p-values of the nonlinear statistics for testing differential expressions of the genetic networks are much smaller than those of the T2 statistic. And furthermore simulations show the Type I errors of the nonlinear statistics agree with the threshold used and the statistics fit the Chi-square distribution.

Associate Editor: Martin Bishop
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