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

Genetics Selection Evolution

Dirk-Jan de Koning⁶  David Waddington⁶  Gwenola Tosser-Klopp⁷  Magali San Cristobal⁷  Christèle Robert-Granié⁷  Marco H Pool²  Daphné Mouzaki⁶  Guillemette Marot⁸  Kim-Anh Lê Cao⁶  Miha Lavrič⁴  Ángeles Jiménez-Marín⁵  Florence Jaffrézic⁸  Ina Hulsegge¹  Juan José Garrido-Pavón⁵  Jean-Louis Foulley⁸  Mylène Duval⁷  Peter Dovč⁴  Céline Delmas⁷  Michael Denis Baron²  Mónica Pérez-Alegre⁵  Michael Watson³ 

[1] Animal Sciences Group Wageningen UR, Lelystad, NL (IDL);Institute for Animal Health, Pirbright, UK (IAH_P);Institute for Animal Health Informatic groups, Compton Laboratory, Compton RG20 7 NN Newbury Bershive, UK;University of Ljubljana, Slovenia (SLN);University of Cordoba, Cordoba, Spain (CDB);Roslin Institute, Roslin, UK (ROSLIN);INRA, Castanet-Tolosan, France (INRA_T);INRA, Jouy-en-Josas, France (INRA_J)

关键词: statistical analysis;    simulation;    two colour microarray;    gene expression;   

Others  :  1093936
DOI  :  10.1186/1297-9686-39-6-669

 received in 2007-05-10, accepted in 2007-07-10,  发布年份 2007

【 摘 要 】

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.

【 授权许可】

   
2007 INRA, EDP Sciences