Omnibus testing and gene filtration in microarray data analysis

Hongying Dai and Richard Charnigo

Journal of Applied Statistics, 2008, vol. 35, issue 1, pages 31-47

Abstract: When thousands of tests are performed simultaneously to detect differentially expressed genes in microarray analysis, the number of Type I errors can be immense if a multiplicity adjustment is not made. However, due to the large scale, traditional adjustment methods require very stringen significance levels for individual tests, which yield low power for detecting alterations. In this work, we describe how two omnibus tests can be used in conjunction with a gene filtration process to circumvent difficulties due to the large scale of testing. These two omnibus tests, the D-test and the modified likelihood ratio test (MLRT), can be used to investigate whether a collection of P-values has arisen from the Uniform(0,1) distribution or whether the Uniform(0,1) distribution contaminated by another Beta distribution is more appropriate. In the former case, attention can be directed to a smaller part of the genome; in the latter event, parameter estimates for the contamination model provide a frame of reference for multiple comparisons. Unlike the likelihood ratio test (LRT), both the D-test and MLRT enjoy simple limiting distributions under the null hypothesis of no contamination, so critical values can be obtained from standard tables. Simulation studies demonstrate that the D-test and MLRT are superior to the AIC, BIC, and Kolmogorov-Smirnov test. A case study illustrates omnibus testing and filtration.

Keywords: multiple comparisons; P-values; Beta contamination model; MMLEs; D-test; MLRT (search for similar items in EconPapers)

Downloads: (external link)
http://www.informawo ... 40C6AD35DC6213A474B5 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Ordering information: This journal article can be ordered from
http://www.tandf.co.uk/journals/subscription.html

Access Statistics for this article

Journal of Applied Statistics is edited by Professor Gopal K. Kanji

More articles in Journal of Applied Statistics from Taylor and Francis Journals
Series data maintained by Christopher F. Baum ().