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Empirical Bayes microarray ANOVA and grouping cell lines by equal expression levels
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
2005 (English)In: Statistical Applications in Genetics and Molecular Biology, ISSN 1544-6115, Vol. 4, no 1, article 7- p.Article in journal (Refereed) Published
Abstract [en]

In the exploding field of gene expression techniques such as DNA microarrays, there are still few general probabilistic methods for analysis of variance. Linear models and ANOVA are heavily used tools in many other disciplines of scientific research. The usual F-statistic is unsatisfactory for microarray data, which explore many thousand genes in parallel, with few replicates. We present three potential one-way ANOVA statistics in a parametric statistical framework. The aim is to separate genes that are differently regulated across several treatment conditions from those with equal regulation. The statistics have different features and are evaluated using both real and simulated data. Our statistic B1 generally shows the best performance, and is extended for use in an algorithm that groups cell lines by equal expression levels for each gene. An extension is also outlined for more general ANOVA tests including several factors. The methods presented are implemented in the freely available statistical language R. They are available at http://www.math.uu.se/staff/pages/?uname=ingrid.

Place, publisher, year, edition, pages
2005. Vol. 4, no 1, article 7- p.
National Category
Probability Theory and Statistics
URN: urn:nbn:se:uu:diva-93239DOI: 10.2202/1544-6115.1125PubMedID: 16646860OAI: oai:DiVA.org:uu-93239DiVA: diva2:166663
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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