Statistical analysis of microarray data: a Bayesian approach.

Details

Serval ID
serval:BIB_5D021BA41873
Type
Article: article from journal or magazin.
Collection
Publications
Title
Statistical analysis of microarray data: a Bayesian approach.
Journal
Biostatistics
Author(s)
Gottardo R., Pannucci J.A., Kuske C.R., Brettin T.
ISSN
1465-4644 (Print)
ISSN-L
1465-4644
Publication state
Published
Issued date
10/2003
Peer-reviewed
Oui
Volume
4
Number
4
Pages
597-620
Language
english
Notes
Publication types: Comparative Study ; Journal Article ; Research Support, U.S. Gov't, P.H.S.
Publication Status: ppublish
Abstract
The potential of microarray data is enormous. It allows us to monitor the expression of thousands of genes simultaneously. A common task with microarray is to determine which genes are differentially expressed between two samples obtained under two different conditions. Recently, several statistical methods have been proposed to perform such a task when there are replicate samples under each condition. Two major problems arise with microarray data. The first one is that the number of replicates is very small (usually 2-10), leading to noisy point estimates. As a consequence, traditional statistics that are based on the means and standard deviations, e.g. t-statistic, are not suitable. The second problem is that the number of genes is usually very large (approximately 10,000), and one is faced with an extreme multiple testing problem. Most multiple testing adjustments are relatively conservative, especially when the number of replicates is small. In this paper we present an empirical Bayes analysis that handles both problems very well. Using different parametrizations, we develop four statistics that can be used to test hypotheses about the means and/or variances of the gene expression levels in both one- and two-sample problems. The methods are illustrated using experimental data with prior knowledge. In addition, we present the result of a simulation comparing our methods to well-known statistics and multiple testing adjustments.
Keywords
Algorithms, Bacillus anthracis/drug effects, Bacillus anthracis/genetics, Bayes Theorem, Carbon Dioxide/pharmacology, Computer Simulation, Data Interpretation, Statistical, Gene Expression Profiling/statistics & numerical data, Oligonucleotide Array Sequence Analysis/statistics & numerical data, ROC Curve, Sample Size
Pubmed
Web of science
Open Access
Yes
Create date
03/03/2022 12:39
Last modification date
23/03/2024 8:24
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