a1 Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
a2 Department of Biological Statistics and Computational Biology, 301 Malott Hall, Cornell University, Ithaca, NY 14853, USA
a3 Department of Food Science, 412 Stocking Hall, Cornell University, Ithaca, NY 14853, USA
a4 Channing Laboratory, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA
We propose an empirical Bayes method based on the extreme value theory (EVT) (BE) for the analysis of data from spotted microarrays where the interest of the investigator (e.g. to identify up-regulated gene markers of a disease) or the design of the experiment (e.g. in certain ‘wild-type versus mutant’ experiments) limits identification of differentially expressed genes to those regulated in a single direction (either up or down). In such experiments, unlike in genome-wide microarrays, analysis is restricted to the tail of the distribution (extremes) of all the genes in the genome. The EVT provides a platform to account for this extreme behaviour, and is therefore a natural candidate for inference about differential expression. We compared the performance of the developed BE method with two other empirical Bayes methods on two real ‘wild-type versus mutant’ datasets where a single direction of regulation was expected due to experimental design, and in a simulation study. The BE method appears to have a better fit to the real data. In the analysis of simulated data, the BE method showed better accuracy and precision while being robust to different characteristics of microarray experiments. The BE method, therefore, seems promising and useful for inference about differential expression in microarrays where either only up- or down-regulated genes are relevant or expected.
(Received January 24 2008)
(Revised May 30 2008)
c1 Corresponding author. Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA. Tel: +1(607) 2533052. Fax. +1 (607) 2533083. e-mail: [email protected]