Document Detail


Should we abandon the t-test in the analysis of gene expression microarray data: a comparison of variance modeling strategies.
MedLine Citation:
PMID:  20838429     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Marine Jeanmougin; Aurelien de Reynies; Laetitia Marisa; Caroline Paccard; Gregory Nuel; Mickael Guedj
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2010-09-03
Journal Detail:
Title:  PloS one     Volume:  5     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2010  
Date Detail:
Created Date:  2010-09-14     Completed Date:  2011-02-18     Revised Date:  2013-05-28    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e12336     Citation Subset:  IM    
Affiliation:
Programme Cartes d'Identité des Tumeurs, Ligue Nationale Contre le Cancer, Paris, France. marine.jeanmougin@genopole.cnrs.fr
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MeSH Terms
Descriptor/Qualifier:
Analysis of Variance
Computer Simulation
Gene Expression Profiling / statistics & numerical data*
Models, Statistical
Oligonucleotide Array Sequence Analysis / statistics & numerical data*
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