We propose a methodological framework to assess heterogeneous patterns of association amongst components of a random vector expressed as a Gaussian directed acyclic graph. The proposed framework is likely to be useful when primary interest focuses on potential contrasts characterizing the association structure between known subgroups of a given sample. We provide inferential frameworks as well as an efficient computational algorithm to fit such a model and illustrate its validity through a simulation. We apply the model to Reverse Phase Protein Array data on Acute Myeloid Leukemia patients to show the contrast of association structure between refractory patients and relapsed patients.


Applied Statistics | Biostatistics | Microarrays | Multivariate Analysis | Statistical Methodology | Statistical Models