Skip Navigation

Biostatistics 2005 6(2):279-291; doi:10.1093/biostatistics/kxi009
This Article
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
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
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Lönnstedt, I.
Right arrow Articles by Britton, T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lönnstedt, I.
Right arrow Articles by Britton, T.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org.

Hierarchical Bayes models for cDNA microarray gene expression

Ingrid Lönnstedt*

Department of Mathematics, Uppsala University, Box 480, Uppsala SE-751 06, Sweden

Tom Britton

Department of Mathematics, Stockholm University, Stockholm SE-106 91, Sweden

* To whom correspondence should be addressed. ingrid{at}math.uu.se

cDNA microarrays are used in many contexts to compare mRNA levels between samples of cells. Microarray experiments typically give us expression measurements on 1000–20 000 genes, but with few replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not satisfactory in this context. A handful of alternative statistics have been developed, including several empirical Bayes methods. In the present paper we present two full hierarchical Bayes models for detecting gene expression, of which one (D) describes our microarray data very well. We also compare the full Bayes and empirical Bayes approaches with respect to model assumptions, false discovery rates and computer running time. The proposed models are compared to existing empirical Bayes models in a simulation study and for a set of data (Yuen et al., 2002), where 27 genes have been categorized by quantitative real-time PCR. It turns out that the existing empirical Bayes methods have at least as good performance as the full Bayes ones.

Keywords: cDNA microarray; Differential expression; Empirical Bayes; Hierarchical Bayes; MCMC simulations; ROC curve


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
 Anticancer ResHome page
J. R. LOPEZ-EGIDO, Y. WANG, M. GRONBERG, P. GRIMFJARD, S. WANG, P. STALBERG, and B. SKOGSEID
Differentially Regulated Genes in MEN1-transfected BON Cells Using RT-differential Display and Oligonucleotide Microarrays
Anticancer Res, June 1, 2009; 29(6): 1859 - 1866.
[Abstract] [Full Text] [PDF]

Home page
 J Exp BotHome page
B. S. Zheng, E. Ronnberg, L. Viitanen, T. A. Salminen, K. Lundgren, T. Moritz, and J. Edqvist
Arabidopsis sterol carrier protein-2 is required for normal development of seeds and seedlings
J. Exp. Bot., September 1, 2008; 59(12): 3485 - 3499.
[Abstract] [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.