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

• Published in: Genome biology 2002-04, Vol.3 (5), p.research0022-research0022, Article research0022.1
• Main Authors: Pan, Wei, Lin, Jizhen, Le, Chap T
• Format: Article
• Language: English
• Subjects: Animals; Gene Expression Profiling - methods; Gene Expression Profiling - standards; Gene Expression Profiling - statistics & numerical data; Gene Expression Regulation - genetics; Models, Genetic; Oligonucleotide Array Sequence Analysis - methods; Oligonucleotide Array Sequence Analysis - standards; Oligonucleotide Array Sequence Analysis - statistics & numerical data; Otitis Media - immunology; Otitis Media - microbiology; Pneumococcal Infections - genetics; Pneumococcal Infections - immunology; Rats; Streptococcus pneumoniae - immunology
• ISSN: 1474-760X; 1465-6906; 1474-760X; 1465-6914
• DOI: 10.1186/gb-2002-3-5-research0022

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. 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. 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.