Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies

Abstract : High-throughput post-genomic studies are now routinely and promisingly investigated in biological and biomedical research. The main statistical approach to select genes differentially expressed between two groups is to apply a t-test, which is subject of criticism in the literature. Numerous alternatives have been developed based on different and innovative variance modeling strategies. However, a critical issue is that selecting a different test usually leads to a different gene list. In this context and given the current tendency to apply the t-test, identifying the most efficient approach in practice remains crucial. To provide elements to answer, we conduct a comparison of eight tests representative of variance modeling strategies in gene expression data: Welch's t-test, ANOVA [1], Wilcoxon's test, SAM [2], RVM [3], limma [4], VarMixt [5] and SMVar [6]. Our comparison process relies on four steps (gene list analysis, simulations, spike-in data and re-sampling) to formulate comprehensive and robust conclusions about test performance, in terms of statistical power, false-positive rate, execution time and ease of use. Our results raise concerns about the ability of some methods to control the expected number of false positives at a desirable level. Besides, two tests (limma and VarMixt) show significant improvement compared to the t-test, in particular to deal with small sample sizes. In addition limma presents several practical advantages, so we advocate its application to analyze gene expression data.
Type de document :
Article dans une revue
PLoS ONE, Public Library of Science, 2010, 5, pp.e12336
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00539115
Contributeur : Grégory Nuel <>
Soumis le : mercredi 24 novembre 2010 - 10:41:59
Dernière modification le : jeudi 9 février 2017 - 15:50:01

Identifiants

  • HAL Id : hal-00539115, version 1

Collections

Citation

Marine Jeanmougin, Aurélien De Reynies, Laetitia Marisa, Caroline Paccard, Gregory Nuel, et al.. Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies. PLoS ONE, Public Library of Science, 2010, 5, pp.e12336. <hal-00539115>

Partager

Métriques

Consultations de la notice

137