Deepak Mav
Back Home Up Next

Russell D. Wolfinger
David Allison
Jenny Bryan
Lai-Har Chi
Jane Chang
Philip Dixon
Kent M. Eskridge
Deborah Glueck
David L. Gold
Susan G. Hilsenbeck
Lawrence Hunter
Rebecka Jornsten
Steen Knudsen
Laura Lazzeroni
Chen-Tuo Liao
Peter Munson
Dan Nettleton
Wei Pan
David M. Rocke
Grace S. Shieh
Lue Ping Zhao
Deepak Mav
Annette Molinaro

Bivariate models for identifying differentially expressed genes in microarray experiments

Deepak Mav and N. Rao Chaganty Department of Mathematics and Statistics Old Dominion University Norfolk, VA 23529-0077

In this paper we present two hierarchical bivariate probability models for the joint distribution of the red and green intensities in cDNA microarrays. The first is a bivariate distribution, with gamma marginals. We assume gamma priors for the scale parameters. Here we use the Bayes estimates of the ratio of the mean intensities to select differentially expressed genes. The second model incorporates in the first model a latent Bernoulli variable, which indicates the presence or absence of differential expression. Here we use the EM algorithm to calculate the posterior probabilities of gene expressions. Using Escherichia coli microarrays data, we show that both models, which account for the correlation between the intensities, are improvements of the models suggested in a seminal paper by Newton et. al. (2001).

Graybill Conference
June 18-20, 2003
University Park Holiday Inn
Fort Collins, CO 80526
email: Fax: (970)491-7895 Phone: (970)491-5269
Last Updated: Wednesday, April 16, 2003