How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach.

Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street, Minneapolis, MN 55455-0378, USA. weip@biostat.umn.edu

BACKGROUND: It has been recognized that replicates of arrays (or spots) may be necessary for reliably detecting differentially expressed genes in microarray experiments. However, the often-asked question of how many replicates are required has barely been addressed in the literature. In general, the answer depends on several factors: a given magnitude of expression change, a desired statistical power (that is, probability) to detect it, a specified Type I error rate, and the statistical method being used to detect the change. Here, we discuss how to calculate the number of replicates in the context of applying a nonparametric statistical method, the normal mixture model approach, to detect changes in gene expression. RESULTS: The methodology is applied to a data set containing expression levels of 1,176 genes in rats with and without pneumococcal middle-ear infection. We illustrate how to calculate the power functions for 2, 4, 6 and 8 replicates. CONCLUSIONS: The proposed method is potentially useful in designing microarray experiments to discover differentially expressed genes. The same idea can be applied to other statistical methods.

PMID: 12049663 [PubMed - indexed for MEDLINE]

PMCID: PMC115224