Meta-analysis of microarray studies to produce an overall gene list is relatively straightforward when complete data are available. When some studies lack information, for example, having only a ranked list of genes instead of complete primary data, it is common to reduce all studies to ranked lists prior to combining them. Since this entails a loss of information, we consider a hierarchical Bayes modeling approach to combining studies using the type of information available in each study: the full data matrix, summary statistics, or ranks for each gene. The model uses an informative prior for the parameter of interest, which eases the detection of differentially expressed genes. Simulations show that the new approach can give substantial power gains compared to classical meta analysis and list aggregation. A large meta-analysis based on 11 published studies providing data of the types cited above is also performed and shows credible results by identifying genes known to be involved in ovarian cancer.