title: Cancer outlier analysis based on mixture modeling of gene expression data.
abstract: Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several methods have been proposed in the context of multiple testing. Such cancer outlier analyses will generally suffer from a serious lack of power, compared with the standard multiple testing setting where common activation of genes across all cancer samples is supposed. In this paper, we consider information sharing across genes and cancer samples, via a parametric normal mixture modeling of gene expression levels of cancer samples across genes after a standardization using the reference, normal sample data. A gene-based statistic for gene selection is developed on the basis of a posterior probability of cancer outlier for each cancer sample. Some efficiency improvement by using our method was demonstrated, even under settings with misspecified, heavy-tailed t-distributions. An application to a real dataset from hematologic malignancies is provided.
authors.affiliation: Department of Statistical Science, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan. kmori@ism.ac.jp
authors.forename: Keita
authors.initials: K
authors.lastname: Mori
authors.forename: Tomonori
authors.initials: T
authors.lastname: Oura
authors.forename: Hisashi
authors.initials: H
authors.lastname: Noma
authors.forename: Shigeyuki
authors.initials: S
authors.lastname: Matsui
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D015972:Gene Expression Regulation, Neoplastic; D019337:Hematologic Neoplasms / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data