Analysis of a simulated microarray dataset : Comparison of methods for data normalisation and detection of differential expression

Autor: Michael Watson, Mónica Pérez-Alegre, Michael Denis Baron, Céline Delmas, Peter Dovč, Mylene Duval, Jean Louis Foulley, Juan José Garrido-Pavon, Ina Hulsegge, Florence Jaffrezic, Miha Lavrič, Kim-Anh Lê Cao, Guillemette Marot, Daphne Mouzaki, Pool, M. H., Christèle Robert-Granié, Magali San Cristobal, Gwenola Tosser-Klopp, David Waddington, Dirk-Jan Koning
Přispěvatelé: Institute for Animal Health, Universidad de Córdoba [Cordoba], Station d'Amélioration Génétique des Animaux (SAGA), Institut National de la Recherche Agronomique (INRA), University of Ljubljana, Station de Génétique Quantitative et Appliquée (SGQA), Wageningen University and Research Centre (WUR), Roslin Institute, Laboratoire de Génétique Cellulaire (LGC), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées
Jazyk: angličtina
Rok vydání: 2007
Předmět:
Zdroj: Genetics Selection Evolution
Genetics Selection Evolution, BioMed Central, 2007, 39 (6), pp.669-683
HAL
ISSN: 0999-193X
1297-9686
Popis: International audience; 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 yetbeen 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.
Databáze: OpenAIRE
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