Manuscript entitled: "Bayesian meta-analysis models for microarray data: a comparative study" Authors: Erin M Conlon, Joon J Song, Anna Liu The following is the WinBUGS code for the probability integration (PI) model for two studies. The parameter T corresponds to the parameter D in the manuscript. The posterior mean of T is used as the basis for inference. The dgamma(alpha, beta) distribution for the precision of the Normal distribution is equivalent to a scaled inverse chi-squared distribution for the variance with parameters (alpha = nu/2, beta = nu/2 * s.squared), where nu = degrees of freedom and s.squared = scale parameter. model { for ( i in 1:N ) { y[1,i] ~ dnorm(xi[1,i], phi1.squared[i]) y[2,i] ~ dnorm(xi[1,i], phi1.squared[i]) y[3,i] ~ dnorm(xi[1,i], phi1.squared[i]) xi[1,i] ~ dnorm(theta1[i], sigma1.squared[i]) y[4,i] ~ dnorm(xi[2,i], phi1.squared[i]) xi[2,i] ~ dnorm(theta1[i], sigma1.squared[i]) y[5,i] ~ dnorm(xi[3,i], phi1.squared[i]) xi[3,i] ~ dnorm(theta1[i], sigma1.squared[i]) y[6,i] ~ dnorm(xi[4,i], phi2.squared[i]) y[7,i] ~ dnorm(xi[4,i], phi2.squared[i]) xi[4,i] ~ dnorm(theta2[i], sigma2.squared[i]) y[8,i] ~ dnorm(xi[5,i], phi2.squared[i]) xi[5,i] ~ dnorm(theta2[i], sigma2.squared[i]) y[9,i] ~ dnorm(xi[6,i], phi2.squared[i]) xi[6,i] ~ dnorm(theta2[i], sigma2.squared[i]) theta1[i] ~ dnorm(0, eta1.squared[i]) eta1.squared[i] <- etas1.squared[T1[i]] theta2[i] ~ dnorm(0, eta2.squared[i]) eta2.squared[i] <- etas2.squared[T1[i]] T[i] ~ dbern(p) T1[i] <- T[i] + 1 } p ~ dbeta(1,1) for ( i in 1:N ){ phi1.squared[i] ~ dgamma(h/2, h/2 * phi1.tilde.squared) phi2.squared[i] ~ dgamma(h/2, h/2 * phi2.tilde.squared) sigma1.squared[i] ~ dgamma(k/2, k/2 * sigma1.tilde.squared) sigma2.squared[i] ~ dgamma(k/2, k/2 * sigma2.tilde.squared) } eta12.squared ~ dgamma(a/2, a/2 * s1.squared) eta22.squared ~ dgamma(a/2, a/2 * s1.squared) etas1.squared[1] <- eta12.squared etas1.squared[2] <- eta12.squared * c1 c1 ~ dgamma(b/2, b/2 * s2.squared) etas2.squared[1] <- eta22.squared etas2.squared[2] <- eta22.squared * c2 c2 ~ dgamma(b/2, b/2 * s2.squared) } The following are sample data, where the rows = slides and the columns = genes. The first 5 slides are for Study 1 as follows: slides 1-3 are for experiment 1, slide 4 is for experiment 2 and slide 5 is for experiment 3. Slides 6-9 are for Study 2 as follows: slides 6 and 7 are for experiment 1, slide 8 is for experiment 2 and slide 9 is for experiment 3. y[ ,1] y[ ,2] y[ ,3] y[ ,4] ... y[ ,N] y[1, ] 0.702716 1.151031 1.033856 1.385349 ... 1.043028 y[2, ] 1.095491 0.976146 1.350054 1.368257 ... 0.845897 y[3, ] 1.354985 1.358365 1.158088 1.041808 ... 1.224347 y[4, ] 0.749297 1.018893 1.146190 1.489410 ... 0.997665 y[5, ] 1.026909 1.153235 1.270153 1.397205 ... 1.462478 y[6, ] 1.234977 1.049516 1.014355 0.298656 ... 1.889275 y[7, ] 1.118606 1.287329 0.827385 0.669852 ... 1.492056 y[8, ] 1.293059 1.079623 0.776515 0.609285 ... 1.238171 y[9, ] 1.408793 1.324384 0.613495 0.091526 ... 1.362053