Use of mixture models in multiple hypothesis testing with applications in bioinformatics

McLachlan, Geoffrey J. and Wockner, Leesa (2010). Use of mixture models in multiple hypothesis testing with applications in bioinformatics. In Hermann Locarek-Junge and Claus Weihs (Ed.), Classification as a Tool for Research: Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft für Klassifikation (pp. 177-184) Heidelberg, Germany: Springer-Verlag. doi:10.1007/978-3-642-10745-0


Author McLachlan, Geoffrey J.
Wockner, Leesa
Title of chapter Use of mixture models in multiple hypothesis testing with applications in bioinformatics
Title of book Classification as a Tool for Research: Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft für Klassifikation
Place of Publication Heidelberg, Germany
Publisher Springer-Verlag
Publication Year 2010
Sub-type Research book chapter (original research)
DOI 10.1007/978-3-642-10745-0
Year available 2009
Series Studies in Classification, Data Analysis, and Knowledge Organization
ISBN 9783642107443
9783642107450
ISSN 1431-8814
Editor Hermann Locarek-Junge
Claus Weihs
Chapter number 18
Start page 177
End page 184
Total pages 8
Total chapters 90
Language eng
Formatted Abstract/Summary
There are many important problems these days where consideration has to be given to carrying out hundreds or even thousands of hypothesis testing problems at the same time. For example, in forming classifiers on the basis of high-dimensional data, the aim might be to select a small subset of useful variables for the prediction problem at hand. In the field of bioinformatics, there are many examples where a large number of hypotheses have to be tested simultaneously. For example, a common problem is the detection of genes that are differentially expressed in a given number of classes. The problem of testing many hypotheses at the same time can be expressed in a two-component mixture framework, using an empirical Bayes approach; see, for example, Efron (2004). In this framework, we present further results as part of an ongoing investigation into the approach of McLachlan et al. (2006) on the adoption of normal mixture models to provide a parametric approach to the estimation of the so-called local false discovery rate. The latter can be viewed as the posterior probability that a given null hypothesis does hold. With this approach, not only can the global false discovery rate be controlled, but also the implied probability of a false negative can be assessed. The methodology is demonstrated on some problems in bioinformatics.
© Springer-Verlag Berlin Heidelberg 2010
Q-Index Code B1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Proceedings of the conference held in Dresden, Germany, 13-18 March 2009.

 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 2 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Wed, 23 Feb 2011, 01:48:30 EST by Kay Mackie on behalf of School of Mathematics & Physics