Bayesian Mixture Model Analysis for Detecting Differentially Expressed Genes
Joint Authors
Source
International Journal of Plant Genomics
Issue
Vol. 2008, Issue 2008 (31 Dec. 2008), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2008-03-25
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Control-treatment design is widely used in microarray gene expression experiments.
The purpose of such a design is to detect genes that express differentially between the control and the treatment.
Many statistical procedures have been developed to detect differentially expressed genes, but all have pros and cons and room is still open for improvement.
In this study, we propose a Bayesian mixture model approach to classifying genes into one of three clusters, corresponding to clusters of downregulated, neutral, and upregulated genes, respectively.
The Bayesian method is implemented via the Markov chain Monte Carlo (MCMC) algorithm.
The cluster means of down- and upregulated genes are sampled from truncated normal distributions whereas the cluster mean of the neutral genes is set to zero.
Using simulated data as well as data from a real microarray experiment, we demonstrate that the new method outperforms all methods commonly used in differential expression analysis.
American Psychological Association (APA)
Jia, Zhenyu& Xu, Shizhong. 2008. Bayesian Mixture Model Analysis for Detecting Differentially Expressed Genes. International Journal of Plant Genomics،Vol. 2008, no. 2008, pp.1-12.
https://search.emarefa.net/detail/BIM-505970
Modern Language Association (MLA)
Jia, Zhenyu& Xu, Shizhong. Bayesian Mixture Model Analysis for Detecting Differentially Expressed Genes. International Journal of Plant Genomics No. 2008 (2008), pp.1-12.
https://search.emarefa.net/detail/BIM-505970
American Medical Association (AMA)
Jia, Zhenyu& Xu, Shizhong. Bayesian Mixture Model Analysis for Detecting Differentially Expressed Genes. International Journal of Plant Genomics. 2008. Vol. 2008, no. 2008, pp.1-12.
https://search.emarefa.net/detail/BIM-505970
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-505970