Title: A Bayesian approach to measure mRNA quality levels for microarray experiments

Authors: Hafiz M.R. Khan, Mohammad A. Faysel, Serge B. Provost

Addresses: Department of Health Informatics, School of Health Related Professions, University of Medicine & Dentistry of New Jersey, 65 Bergen Street, Newark, NJ 07101-1709, USA. ' Department of Health Informatics, School of Health Related Professions, University of Medicine & Dentistry of New Jersey, 65 Bergen Street, Newark, NJ 07101-1709, USA. ' Department of Statistical & Actuarial Sciences, The University of Western Ontario London, Ontario N6A 5B7, Canada

Abstract: In this paper, we focus our attention on a novel Bayesian approach to determine mRNA levels from quality control indicators (QCI), such as Background, Percent Present, Scale Factor GAPDH and ß-Actin. QCI data can be modelled as symmetrical and asymmetrical probability distributions. We discuss the identification of statistical probability models from QCI sample data sets as well as the derivation of mRNA predictive distributions by making use of a novel Bayesian approach in this context.

Keywords: microarrays; quality control indicators; QCI; Affymetrix; probability models; Bayesian approach; posterior distribution; predictive distribution; mRNA quality levels; medical informatics.

DOI: 10.1504/IJMEI.2008.019475

International Journal of Medical Engineering and Informatics, 2008 Vol.1 No.1, pp.125 - 133

Published online: 13 Jul 2008 *

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