title: Split-plot microarray experiments: issues of design, power and sample size.
abstract: This article focuses on microarray experiments with two or more factors in which treatment combinations of the factors corresponding to the samples paired together onto arrays are not completely random. A main effect of one (or more) factor(s) is confounded with arrays (the experimental blocks). This is called a split-plot microarray experiment. We utilise an analysis of variance (ANOVA) model to assess differentially expressed genes for between-array and within-array comparisons that are generic under a split-plot microarray experiment. Instead of standard t- or F-test statistics that rely on mean square errors of the ANOVA model, we use a robust method, referred to as 'a pooled percentile estimator', to identify genes that are differentially expressed across different treatment conditions. We illustrate the design and analysis of split-plot microarray experiments based on a case application described by Jin et al. A brief discussion of power and sample size for split-plot microarray experiments is also presented.
authors.affiliation: Division of Biostatistics and Bioinformatics, National Health Research Institutes, Taipei, Taiwan, Republic of China.
authors.forename: Pi-Wen
authors.initials: PW
authors.lastname: Tsai
authors.forename: Mei-Ling Ting
authors.initials: ML
authors.lastname: Lee
mesh_terms: D000704:Analysis of Variance; D002675:Child, Preschool; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D005260:Female; D005838:Genotype; D006801:Humans; D007223:Infant; D008297:Male; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012107:Research Design; D018401:Sample Size; D013223:Statistics as Topic