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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)
  • Published : 2006.12.31

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.

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