The "Window t-test": a simple and powerful approach to detect differentially expressed genes in microarray datasets

Fabrice Berger, Benoit De Hertogh, Michael Pierre, Anthoula Gaigneaux, Eric Depiereux

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This work focuses on differential expression analysis of microarray datasets. One way to improve such statistical analyses is to integrate biological information in the design of these analyses. In this paper, we will use the relationship between the level of gene expression and variability. Using this biological information, we propose to integrate the information from multiple genes to get a better estimate of individual gene variance, when a small number of replicates are available, to increase the power of the statistical analysis. We describe a strategy named the "Window t test" that uses multiple genes which share a similar expression level to compute the variance which is then incorporated a classic t test. The performances of this new method are evaluated by comparison with classic and widely- used methods for differential expression analysis (the classic Student t test, the Regularized t test (reg t test), SAM, Limma, LPE and Shrinkage t). In each case tested, the results obtained were at least equivalent to the best performing method and, in most cases, outperformed it. Moreover, the Window t test relies on a very simple procedure requiring small computing power compared with other methods designed for microarray differential expression analysis.
langue originaleAnglais
Pages (de - à)327-344
Nombre de pages18
journalCentral European Journal of Biology
Numéro de publication3
Etat de la publicationPublié - 2008

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