A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

McLachlan, GJ, Bean, RW and Jones, LBT (2006) A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics, 22 13: 1608-1615.


Author McLachlan, GJ
Bean, RW
Jones, LBT
Title A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays
Journal name Bioinformatics  (ERA 2012 Listed)    (ERA 2010 Rank A*)   Check publisher's open access policy
Publication date 2006
Sub-type Article
DOI 10.1093/bioinformatics/btl148
Volume number 22
Issue number 13
ISSN 1367-4803
Start page 1608
End page 1615
Total pages 8
Editor A Bateman
A Valencia
Place of publication Oxford
Publisher Oxford Univ Press
Collection year 2006
Language eng
Subject C1
321011 Medical Genetics
780101 Mathematical sciences
780105 Biological sciences
730305 Diagnostic methods
230204 Applied Statistics
270201 Gene Expression
Abstract Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.
Keyword Mathematics, Interdisciplinary Applications
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Statistics & Probability
False Discovery Rate
Model
Permutation
Rates
Mathematical & Computational Biology
Q-Index Code C1

 
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Created: Wed, 15 Aug 2007, 08:18:58 EST