title: Sample sizes for a robust ranking and selection of genes in microarray experiments.
abstract: The main role of high-throughput microarrays today is screening of relevant genes from a large pool of candidate genes. For prioritizing genes for subsequent studies, gene ranking based on the strength of the association with the phenotype is a relevant statistical output. In this article, we propose sample size calculations based on gene ranking and selection using the non-parametric Mann-Whitney-Wilcoxon statistic in microarray experiments. The use of the non-parametric statistic is expected to be advantageous in robustification in gene ranking for the deviation from normality and for possible scale change by using different platforms such as polymerase chain reaction-based platforms in subsequent studies in gene expression data. Application to the data set from a clinical study for lymphoma is given.
authors.affiliation: Department of Data Science, The Institute of Statistical Mathematics, Tokyo, Japan. smatsui@ism.ac.jp
authors.forename: Shigeyuki
authors.initials: S
authors.lastname: Matsui
authors.forename: Tomonori
authors.initials: T
authors.lastname: Oura
mesh_terms: D015870:Gene Expression; D006801:Humans; D008223:Lymphoma / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D018401:Sample Size; D018709:Statistics, Nonparametric