  We develop novel hierarchical reciprocal graphical models to infer gene
networks from heterogeneous data. In the case of data that can be naturally
divided into known groups, we propose to connect graphs by introducing a
hierarchical prior across group-specific graphs, including a correlation on
edge strengths across graphs. Thresholding priors are applied to induce
sparsity of the estimated networks. In the case of unknown groups, we cluster
subjects into subpopulations and jointly estimate cluster-specific gene
networks, again using similar hierarchical priors across clusters. We
illustrate the proposed approach by simulation studies and two applications
with multiplatform genomic data for multiple cancers.
