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

• Published in: PloS one Vol. 5; no. 9; p. e12336
• Main Authors: Jeanmougin, Marine, de Reynies, Aurelien, Marisa, Laetitia, Paccard, Caroline, Nuel, Gregory, Guedj, Mickael
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.09.2010; Public Library of Science (PLoS)
• Subjects: Analysis of Variance; Applied mathematics; Bioinformatics; Breast cancer; Cancer; Comparative analysis; Computer Simulation; Control methods; Data processing; DNA microarrays; Gene expression; Gene Expression Profiling - statistics & numerical data; Genes; Genetics and Genomics/Bioinformatics; Genetics and Genomics/Gene Expression; Genomes; Genomics; Head & neck cancer; Leukemia; Lymphoma; Mathematics; Mathematics/Statistics; Metastasis; Modelling; Models, Statistical; Molecular Biology/Bioinformatics; Oligonucleotide Array Sequence Analysis - statistics & numerical data; Power; Probability; Statistics; Student's t-test; Studies; Variance; Variance analysis; France

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 alterna...