The importance of incorporating existing biological knowledge, such as gene functional annotations in gene ontology, in analysing high throughput genomic and proteomic data is being increasingly recognized. In the context of detecting differential gene expression, however, the current practice of using gene annotations is limited primarily to validations. Here we take a direct approach to incorporating gene annotations into mixture models for analysis. First, in contrast with a standard mixture model assuming that each gene of the genome has the same distribution, we study stratified mixture models allowing genes with different annotations to have different distributions, such as prior probabilities. Second, rather than treating parameters in stratified mixture models independently, we propose a hierarchical model to take advantage of the hierarchical structure of most gene annotation systems, such as gene ontology. We consider a simplified implementation for the proof of concept. An application to a mouse microarray data set and a simulation study demonstrate the improvement of the two new approaches over the standard mixture model.
|Original language||English (US)|
|Number of pages||16|
|Journal||Journal of the Royal Statistical Society. Series C: Applied Statistics|
|State||Published - May 2006|
Copyright 2018 Elsevier B.V., All rights reserved.
- Density estimation
- Empirical Bayes method
- False discovery rate
- Gene ontology
- Hierarchical model
- Mixture model