Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication)

Watson, Michael, Pérez-Alegre, Mónica, Baron, Michael Denis, Delmas, Céline, Dovč, Peter, Duval, Mylène, Foulley, Jean-Louis, Garrido-Pavón, Juan José, Hulsegge, Ina, Jaffrézic, Florence, Jiménez-Marín, Ángeles, Lavrič, Miha, Lê Cao, Kim-Anh, Marot, Guillemette, Mouzaki, Daphné, Pool, Marco H., Robert-Granié, Christèle, San Cristobal, Magali, Tosser-Klopp, Gwenola, Waddington, David and de Koning, Dirk-Jan (2007) Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication). Genetics Selection Evolution, 39 6: 669-683. doi:10.1051/gse:2007031


Author Watson, Michael
Pérez-Alegre, Mónica
Baron, Michael Denis
Delmas, Céline
Dovč, Peter
Duval, Mylène
Foulley, Jean-Louis
Garrido-Pavón, Juan José
Hulsegge, Ina
Jaffrézic, Florence
Jiménez-Marín, Ángeles
Lavrič, Miha
Lê Cao, Kim-Anh
Marot, Guillemette
Mouzaki, Daphné
Pool, Marco H.
Robert-Granié, Christèle
San Cristobal, Magali
Tosser-Klopp, Gwenola
Waddington, David
de Koning, Dirk-Jan
Title Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication)
Journal name Genetics Selection Evolution   Check publisher's open access policy
ISSN 0999-193X
1297-9686
Publication date 2007-12
Sub-type Article (original research)
DOI 10.1051/gse:2007031
Open Access Status DOI
Volume 39
Issue 6
Start page 669
End page 683
Total pages 15
Place of publication Paris, France
Publisher EDP Sciences
Language eng
Subject 1001 Agricultural Biotechnology
0104 Statistics
0604 Genetics
Abstract Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set. © INRA.
Keyword Gene expression
Simulation
Statistical analysis
Two colour microarray
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
Institute for Molecular Bioscience - Publications
 
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Created: Fri, 28 May 2010, 15:52:26 EST by Mary-Anne Marrington on behalf of Institute for Molecular Bioscience