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Hierarchical Bayes models for cDNA microarray gene expression
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
2005 (English)In: Biostatistics, ISSN 1465-4644, E-ISSN 1468-4357, Vol. 6, no 2, 279-291 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2005. Vol. 6, no 2, 279-291 p.
National Category
Probability Theory and Statistics
URN: urn:nbn:se:uu:diva-93238DOI: 10.1093/biostatistics/kxi009PubMedID: 15772106OAI: oai:DiVA.org:uu-93238DiVA: diva2:166662
Available from: 2005-06-02 Created: 2005-06-02 Last updated: 2013-08-01Bibliographically approved
In thesis
1. Empirical Bayes Methods for DNA Microarray Data
Open this publication in new window or tab >>Empirical Bayes Methods for DNA Microarray Data
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

cDNA microarrays is one of the first high-throughput gene expression technologies that has emerged within molecular biology for the purpose of functional genomics. cDNA microarrays compare the gene expression levels between cell samples, for thousands of genes simultaneously.

The microarray technology offers new challenges when it comes to data analysis, since the thousands of genes are examined in parallel, but with very few replicates, yielding noisy estimation of gene effects and variances. Although careful image analyses and normalisation of the data is applied, traditional methods for inference like the Student t or Fisher’s F-statistic fail to work.

In this thesis, four papers on the topics of empirical Bayes and full Bayesian methods for two-channel microarray data (as e.g. cDNA) are presented. These contribute to proving that empirical Bayes methods are useful to overcome the specific data problems. The sample distributions of all the genes involved in a microarray experiment are summarized into prior distributions and improves the inference of each single gene.

The first part of the thesis includes biological and statistical background of cDNA microarrays, with an overview of the different steps of two-channel microarray analysis, including experimental design, image analysis, normalisation, cluster analysis, discrimination and hypothesis testing. The second part of the thesis consists of the four papers. Paper I presents the empirical Bayes statistic B, which corresponds to a t-statistic. Paper II is based on a version of B that is extended for linear model effects. Paper III assesses the performance of empirical Bayes models by comparisons with full Bayes methods. Paper IV provides extensions of B to what corresponds to F-statistics.

Place, publisher, year, edition, pages
Uppsala: Matematiska institutionen, 2005. xvi + 45 p.
Uppsala Dissertations in Mathematics, ISSN 1401-2049 ; 40
Mathematical statistics, two-channel microarrays, differential expression, replication, empirical Bayes, factorial design, interaction, time trends, hierarchical Bayes, MCMC simulations, ANOVA, F-statistics, Matematisk statistik
National Category
Probability Theory and Statistics
urn:nbn:se:uu:diva-5865 (URN)91-506-1807-5 (ISBN)
Public defence
2005-09-16, MIC-aulan, Hus 6, Polacksbacken, Uppsala, 10:15
Available from: 2005-06-02 Created: 2005-06-02Bibliographically approved

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