Bayesian classification for bivariate normal gene expression

Authors:
Sandra Ramos;Antónia Amaral Turkman;Marília Antunes
Affiliations:
High Institute of Engineering of Oporto, Department of Mathematics - ISEP, 4200-072 Porto, Portugal and Center of Statistics and Applications, Lisbon, Portugal;University of Lisbon, Faculty of Sciences, Department of Statistics and Operations Research, Portugal and Center of Statistics and Applications, Lisbon, Portugal;University of Lisbon, Faculty of Sciences, Department of Statistics and Operations Research, Portugal and Center of Statistics and Applications, Lisbon, Portugal
Venue:
Computational Statistics & Data Analysis
Year:
2010

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Abstract

A Bayesian optimal screening method (BOSc) is proposed to classify an individual into one of two groups, based on the observation of pairs of covariates, namely the expression level of pairs of genes (previously selected by a specific method, among the thousands of genes present in the microarray) measured using DNA microarrays technology. The method is general and can be applied to any correlated pair of screening variables, either with a bivariate normal distribution or which can be transformed into a bivariate normal. Results on microarray data sets (Leukemia, Prostate and Breast) show that BOSc performance is competitive with, and in some cases significantly better than, quadratic and linear discriminant analyses and support vector machines classifiers. BOSc provides flexible parametric decision rules. Finally, the screening classifier allows the calculation of operating characteristics while addressing information about the prevalence of the disease or type of disease, which is an advantage over other classification methods.