A Bayesian approach to measure mRNA quality levels for microarray experiments

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 s-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.

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