Heterogeneous Reciprocal Graphical Models

• Published in: Biometrics Vol. 74; no. 2; pp. 606 - 615
• Main Authors: Ni, Yang, Müller, Peter, Zhu, Yitan, Ji, Yuan
• Format: Journal Article
• Language: English
• Published: United States Wiley-Blackwell 01.06.2018; Blackwell Publishing Ltd
• Subjects: BIOMETRIC METHODOLOGY: DISCUSSION PAPER; biometry; Biometry - methods; Cluster Analysis; Clusters; Computer Simulation; Dirichlet‐multinomial allocation; Gene Regulatory Networks; Genomics; Graphs; Hierarchical model; Humans; Model‐based clustering; Multiplatform Genomic Data; neoplasms; Neoplasms - genetics; Networks; Pitman–Yor process; Subpopulations; Thresholding prior; Multiplatform Genomic Data; Hierarchical model; Pitman-Yor process; Thresholding prior; Dirichlet-multinomial allocation; Model-based clustering
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.12791

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 three applications with multiplatform genomic data for multiple cancers.