{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T19:23:38Z","timestamp":1749756218073,"version":"3.32.0"},"reference-count":10,"publisher":"Oxford University Press (OUP)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p><jats:p>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 \u03c72 distribution.<\/jats:p><jats:p>Contact: \u00a0hxiong@cs.tamu.edu<\/jats:p><jats:p>Supplementary information: Supplementary data are available on Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl034","type":"journal-article","created":{"date-parts":[[2006,2,11]],"date-time":"2006-02-11T01:23:56Z","timestamp":1139621036000},"page":"919-923","source":"Crossref","is-referenced-by-count":20,"title":["Non-linear tests for identifying differentially expressed genes or genetic networks"],"prefix":"10.1093","volume":"22","author":[{"given":"Hao","family":"Xiong","sequence":"first","affiliation":[{"name":"Department of Computer Science, Texas A&M University \u00a0 301 Harvey R. Bright Bldg, College Station, TX 77843-3112, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2006,10,2]]},"reference":[{"key":"2023012409211048400_b1","volume-title":"An Introduction to Multivariate Statistical Analysis","author":"Anderson","year":"1984","edition":"Edn 2"},{"key":"2023012409211048400_b2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1980.tb01094.x","article-title":"Relative curvature measure of nonlinearity","volume":"42","author":"Bates","year":"1980","journal-title":"J. R. Statist. Soc. B"},{"key":"2023012409211048400_b3","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/4462","article-title":"Exploring the new world of the genome with DNA microarrays","volume":"21","author":"Brown","year":"1999","journal-title":"Nat. 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