Bayesian models based on test statistics for multiple hypothesis testing problems |
| |
Authors: | Ji Yuan Lu Yiling Mills Gordon B |
| |
Institution: | 1Department of Bioinformatics and Computational Biology and 2Department of Systems Biology, The University of Texas, M. D. Anderson Cancer Center, Houston, TX 77030, USA |
| |
Abstract: | Motivation: We propose a Bayesian method for the problem ofmultiple hypothesis testing that is routinely encountered inbioinformatics research, such as the differential gene expressionanalysis. Our algorithm is based on modeling the distributionsof test statistics under both null and alternative hypotheses.We substantially reduce the complexity of the process of definingposterior model probabilities by modeling the test statisticsdirectly instead of modeling the full data. Computationally,we apply a Bayesian FDR approach to control the number of rejectionsof null hypotheses. To check if our model assumptions for thetest statistics are valid for various bioinformatics experiments,we also propose a simple graphical model-assessment tool. Results: Using extensive simulations, we demonstrate the performanceof our models and the utility of the model-assessment tool.In the end, we apply the proposed methodology to an siRNA screeningand a gene expression experiment. Contact: yuanji{at}mdanderson.org Supplementary information: Supplementary data are availableat Bioinformatics online.
Associate Editor: Chris Stoeckert |
| |
Keywords: | |
本文献已被 PubMed Oxford 等数据库收录! |
|