  In many applications, multivariate samples may harbor previously unrecognized
heterogeneity at the level of conditional independence or network structure.
For example, in cancer biology, disease subtypes may differ with respect to
subtype-specific interplay between molecular components. Then, both subtype
discovery and estimation of subtype-specific networks present important and
related challenges. To enable such analyses, we put forward a mixture model
whose components are sparse Gaussian graphical models. This brings together
model-based clustering and graphical modeling to permit simultaneous estimation
of cluster assignments and cluster-specific networks. We carry out estimation
within an L1-penalized framework, and investigate several specific penalization
regimes. We present empirical results on simulated data and provide general
recommendations for the formulation and use of mixtures of L1-penalized
Gaussian graphical models.
