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Bioinformatics Advance Access originally published online on May 26, 2006
Bioinformatics 2006 22(19):2446-2451; doi:10.1093/bioinformatics/btl173
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Comments on ‘Bayesian hierarchical error model for analysis of gene expression data’

Xiao-Lin Wu 1,{dagger}, Larry J. Forney 1,* and Paul Joyce 2

1 Department of Biological Sciences, University of Idaho Moscow ID 83844-3051, USA
2 Department of Mathematics and Statistics, University of Idaho Moscow ID 83844-1103, USA

*To whom correspondence should be addressed. Email: lforney@uidaho.edu

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Cho and Lee (2004) proposed a Bayesian hierarchical error model (HEM) to account for heterogeneous error variability in oligonucleotide microarray experiments. They estimated the parameters of their model using Markov Chain Monte Carlo (MCMC) and proposed an F-like summary statistic to identify differentially expressed genes under multiple conditions. Their HEM is one of the emerging Bayesian hierarchical modeling tools that have been developed for the analysis of multiple-level data structures and variation in microarray gene expression data (Broet et al., 2002; Tadesse and Ibrahim, 2004; Cho and Lee, 2004). In this letter, we first discuss the significance of the HEM developed by Cho and Lee. Then, we re-derive the fully conditional distributions for gene and conditional effects, since we think that these two fully conditional distributions were not presented properly in their paper. Finally, we expand the HEM to deal with biological or/and experimental . . . [Full Text of this Article]


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H. Cho and J. K. Lee
Response to comments on 'Bayesian Hierarchical Error Model for Analysis of Gene Expression Data'
Bioinformatics, October 1, 2006; 22(19): 2452 - 2452.
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