Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 362919
Title Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differntial expression
Author(s) Watson, M.; Perez-Alegre, M.; Denis Baron, M.; Delmas, C.; Dovc, P.; Duval, M.; Foulley, J.L.; Garrido-Pavon, J.J.; Hulsegge, B.; Jafrezic, F.; Jiménez-Marín, A.; Lavric, M.; Lê Cao, K.A.; Marot, G.; Mouzaki, D.; Pool, M.H.; Robert-Granié, C.; San Cristobal, M.; Tosser-Klop, G.; Waddington, D.; Koning, D.J. de
Source Genetics, Selection, Evolution 39 (2007)6. - ISSN 0999-193X - p. 669 - 683.
DOI https://doi.org/10.1051/gse:2007031
Department(s) Livestock Research
Publication type Refereed Article in a scientific journal
Publication year 2007
Keyword(s) gene-expression - dna-microarray - model
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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