Bioinformatics Advance Access originally published online on February 10, 2006
Bioinformatics 2006 22(8):919-923; doi:10.1093/bioinformatics/btl034
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Non-linear tests for identifying differentially expressed genes or genetic networks
Department of Computer Science, Texas A&M University 301 Harvey R. Bright Bldg, College Station, TX 77843-3112, USA
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 non-linear tests is presented, and two specific non-linear test statistics that use non-linear transformations of means are developed. Asymptotical distributions of the non-linear test statistics under the null and alternative hypothesis are derived. It has been proved that under some conditions the power of the non-linear test statistics is higher than that of the T2 statistic. Besides theory, to evaluate in practice the performance of the non-linear test statistics, they are applied to two real datasets. The preliminary results demonstrate that the P-values of the non-linear 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 non-linear statistics agree with the threshold used and the statistics fit the 
2 distribution.
Contact: hxiong{at}cs.tamu.edu
Supplementary information: Supplementary data are available on Bioinformatics online.
Received on September 19, 2005; revised on January 23, 2006; accepted on January 31, 2006
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