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http://dx.doi.org/10.5351/CKSS.2006.13.3.701

Statistical Method for Implementing the Experimenter Effect in the Analysis of Gene Expression Data  

Kim, In-Young (Department of Epidemiology and Public Health, School of Medicine, Yale university)
Rha, Sun-Young (Brain Korea 21 Project for Medical Science, College of Medicine, Yonsei University)
Kim, Byung-Soo (Department of Applied Statistics, Yonsei University)
Publication Information
Communications for Statistical Applications and Methods / v.13, no.3, 2006 , pp. 701-718 More about this Journal
Abstract
In cancer microarray experiments, the experimenter or patient which is nested in each experimenter often shows quite heterogeneous error variability, which should be estimated for identifying a source of variation. Our study describes a Bayesian method which utilizes clinical information for identifying a set of DE genes for the class of subtypes as well as assesses and examines the experimenter effect and patient effect which is nested in each experimenter as a source of variation. We propose a Bayesian multilevel mixed effect model based on analysis of covariance (ANACOVA). The Bayesian multilevel mixed effect model is a combination of the multilevel mixed effect model and the Bayesian hierarchical model, which provides a flexible way of defining a suitable correlation structure among genes.
Keywords
Analysis of covariance; Bayesian hierarchical model; cDNA microarray; multilevel mixed effect model;
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