Skip Navigation



Bioinformatics Advance Access published online on March 25, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth192
Bioinformatics © Oxford University Press 2004; all rights reserved
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
20/13/2016    most recent
bth192v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Cho, H.
Right arrow Articles by Lee, J. K.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Cho, H.
Right arrow Articles by Lee, J. K.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Received June 10, 2003
Revised February 17, 2004
Accepted February 27, 2004

Article

Bayesian hierarchical error model for analysis of gene expression data

HyungJun Cho 1* Jae K. Lee 1

1 Hospital West Complex, Rm. 3181, P.O. Box 800717, Division of Biostatistics and Epidemiology, Department of Health Evaluation Sciences, University of Virginia School of Medicine, Charlottesville, VA 22908-0717

* To whom correspondence should be addressed. E-mail: hcho{at}virginia.edu.


  Abstract

Motivation: Analysis of genome-wide microarray data requires the estimation of a large number of genetic parameters for individual genes and their interaction expression patterns under multiple biological conditions. The sources of microarray error variability comprise various biological and experimental factors, such as biological and individual replication, sample preparation, hybridization, and image processing. Moreover, the same gene often shows quite heterogeneous error variability under different biological and experimental conditions, which must be separately estimated for evaluating the statistical significance of differential expression patterns. Widely-used linear modeling approaches are limited because they do not allow simultaneous modeling and inference on the large number of these genetic parameters and heterogeneous error components on different genes, different biological and experimental conditions, and varying intensity ranges in microarray data.

Results: We propose a Bayesian hierarchical error model (HEM) to overcome the above restrictions. HEM accounts for heterogeneous error variability in an oligonucleotide microarray experiment. The error variability is decomposed into two components (experimental and biological errors) when both biological and experimental replicates are available. Our HEM inference is based on Markov chain Monte Carlo (MCMC) to estimate a large number of parameters from a single likelihood function for all genes. An F-like summary statistic is proposed to identify differentially expressed genes under multiple conditions based on the HEM estimation. The performance of HEM and its F-like statistic was examined with simulated data and two published microarray data sets--primate brain data and mouse B-cell development data. HEM was also compared with ANOVA using simulated data.

Availability: The software for the hierarchical error model is available from the authors upon request.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
C. Murie and R. Nadon
A correction for estimating error when using the Local Pooled Error Statistical Test
Bioinformatics, August 1, 2008; 24(15): 1735 - 1736.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
T. Park, Y. Kim, S. Bekiranov, and J. K. Lee
Error-pooling-based statistical methods for identifying novel temporal replication profiles of human chromosomes observed by DNA tiling arrays
Nucleic Acids Res., May 14, 2007; 35(9): e69 - e69.
[Abstract] [Full Text] [PDF]

Home page
 Physiol. GenomicsHome page
H. J. Cho, P. Shashkin, C. A. Gleissner, D. Dunson, N. Jain, J. K. Lee, Y. Miller, and K. Ley
Induction of dendritic cell-like phenotype in macrophages during foam cell formation
Physiol Genomics, April 24, 2007; 29(2): 149 - 160.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
X.-L. Wu, L. J. Forney, and P. Joyce
Comments on 'Bayesian hierarchical error model for analysis of gene expression data'
Bioinformatics, October 1, 2006; 22(19): 2446 - 2451.
[Full Text] [PDF]

Home page
 BioinformaticsHome page
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.
[Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.