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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14659

Title: Bayesian variable selection method for modeling dose-response microarray data under simple order restrictions
Authors: Otava, Martin
Kasim, Adetayo
Shkedy, Ziv
Lin, Dan
Kato, Bernet
Issue Date: 2012
Citation: Komárek, Arnošt; Nagy, Stanislav (Ed.). Proceedings of the 27nd International Workshop on Statistical Modelling (IWSM), p. 673-679
Abstract: The aim of the analysis presented below is to investigate dose-response relationship in a microarray setting. Typically, in dose-response experiments the outcome of interest is measured in several (increasing) dose levels and the goal of the analysis is to establish the relationship which represents the dependency of the response on dose. Bayesian modeling of dose-response microarray data offers the possibility to jointly establish the dose response relationships between gene expression and increasing doses of therapeutic compound and to determine the nature of the relationships wherever it exists. The Bayesian variable selection approach provides a modeling framework that allows estimating the posterior probabilities for a given set of pre-specified models and in particular the posterior probability of the model estimated under the null hypothesis of no dose effect. The posterior probabilities are used for multiplicity adjustment using the direct posterior probability approach.
URI: http://hdl.handle.net/1942/14659
Category: C2
Type: Proceedings Paper
Appears in Collections: Research publications

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