title: Microarray data analysis: a hierarchical T-test to handle heteroscedasticity.
abstract: The analysis of differential gene expression in microarray experiments requires the development of adequate statistical tools. This article describes a simple statistical method for detecting differential expression between two conditions with a low number of replicates. When comparing two group means using a traditional t-test, gene-specific variance estimates are unstable and can lead to wrong conclusions. We construct a likelihood ratio test while modelling these variances hierarchically across all genes, and express it as a t-test statistic. By borrowing information across genes we can take advantage of their large numbers, and still yield a gene-specific test statistic. We show that this hierarchical t-test is more powerful than its traditional version and generates less false positives in a simulation study, especially with small sample sizes. This approach can be extended to cases where there are more than two groups.
authors.affiliation: Department of Medical Statistics, Leiden University Medical Center, PO Box 9604, 2300 RC Leiden, The Netherlands. r.x.menezes@lumc.nl
authors.forename: Renée X
authors.initials: RX
authors.lastname: de Menezes
authors.forename: Judith M
authors.initials: JM
authors.lastname: Boer
authors.forename: Hans C
authors.initials: HC
authors.lastname: van Houwelingen
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size; D016008:Statistical Distributions