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Statistical Method for Implementing the Experimenter Effect in the Analysis of Gene Expression Data
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 Title & Authors
Statistical Method for Implementing the Experimenter Effect in the Analysis of Gene Expression Data
Kim, In-Young; Rha, Sun-Young; Kim, Byung-Soo;
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 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;
 Language
English
 Cited by
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