Split-plot microarray experiments: issues of design, power and sample size.
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.
Tsai
Pi-Wen
PW
Division of Biostatistics and Bioinformatics, National Health Research Institutes, Taipei, Taiwan, Republic of China.
Lee
Mei-Ling Ting
ML
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