Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Empirical Bayes Methods for DNA Microarray Data
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics.
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. , p. xvi + 45
Series
Uppsala Dissertations in Mathematics, ISSN 1401-2049 ; 40
Keywords [en]
Mathematical statistics, two-channel microarrays, differential expression, replication, empirical Bayes, factorial design, interaction, time trends, hierarchical Bayes, MCMC simulations, ANOVA, F-statistics
Keywords [sv]
Matematisk statistik
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-5865ISBN: 91-506-1807-5 (print)OAI: oai:DiVA.org:uu-5865DiVA, id: diva2:166664
Public defence
2005-09-16, MIC-aulan, Hus 6, Polacksbacken, Uppsala, 10:15
Opponent
Supervisors
Available from: 2005-06-02 Created: 2005-06-02Bibliographically approved
List of papers
1. Replicated microarray data
Open this publication in new window or tab >>Replicated microarray data
2002 (English)In: Statistica sinica, ISSN 1017-0405, E-ISSN 1996-8507, Vol. 12, no 1, p. 31-46Article in journal (Refereed) Published
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-93236 (URN)
Available from: 2005-06-02 Created: 2005-06-02 Last updated: 2017-12-14Bibliographically approved
2. Microarray analysis of two interacting treatments: a linear model and trends in expression over time
Open this publication in new window or tab >>Microarray analysis of two interacting treatments: a linear model and trends in expression over time
Article in journal (Refereed) In press
Identifiers
urn:nbn:se:uu:diva-93237 (URN)
Available from: 2005-06-02 Created: 2005-06-02 Last updated: 2010-01-14Bibliographically approved
3. Hierarchical Bayes models for cDNA microarray gene expression
Open this publication in new window or tab >>Hierarchical Bayes models for cDNA microarray gene expression
2005 (English)In: Biostatistics, ISSN 1465-4644, E-ISSN 1468-4357, Vol. 6, no 2, p. 279-291Article 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.

National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-93238 (URN)10.1093/biostatistics/kxi009 (DOI)15772106 (PubMedID)
Available from: 2005-06-02 Created: 2005-06-02 Last updated: 2017-12-14Bibliographically approved
4. Empirical Bayes microarray ANOVA and grouping cell lines by equal expression levels
Open this publication in new window or tab >>Empirical Bayes microarray ANOVA and grouping cell lines by equal expression levels
2005 (English)In: Statistical Applications in Genetics and Molecular Biology, ISSN 1544-6115, E-ISSN 1544-6115, Vol. 4, no 1, article id 7Article 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.

National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-93239 (URN)10.2202/1544-6115.1125 (DOI)000238478100031 ()16646860 (PubMedID)
Available from: 2005-06-02 Created: 2005-06-02 Last updated: 2017-12-14Bibliographically approved

Open Access in DiVA

fulltext(1314 kB)2246 downloads
File information
File name FULLTEXT01.pdfFile size 1314 kBChecksum MD5
bb616886e139529e77ce8178b861e45be2d854d34617e47694b14e0c28911d37e53bd153
Type fulltextMimetype application/pdf

By organisation
Department of Mathematics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 2246 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 2181 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf