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

Author(s): Watson Michael | Pérez-Alegre Mónica | Baron Michael | Delmas Céline | Dovč Peter | Duval Mylène | Foulley Jean-Louis | Garrido-Pavón Juan | Hulsegge Ina | Jaffrézic Florence | Jiménez-Marín Ángeles | Lavrič Miha | Lê Cao Kim-Anh | Marot Guillemette | Mouzaki Daphné | Pool Marco | Robert-Granié Christèle | San Cristobal Magali | Tosser-Klopp Gwenola | Waddington David | de Koning Dirk-Jan

Journal: Genetics Selection Evolution
ISSN 0999-193X

Volume: 39;
Issue: 6;
Start page: 669;
Date: 2007;
Original page

Keywords: gene expression | two colour microarray | simulation | statistical analysis

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.
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