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Abstract:
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Gaussian graphical models represent a good tool for capturing interactions between various entities. However, in many applications in biology one is interested in modeling associations both between, as well as within molecular compartments (e.g. interactions between genes and proteins/metabolites). To this end, inferring multi-layered network structures from high-dimensional data provides insight into understanding the conditional relationships among nodes within layers, after adjusting for and quantifying the effects of nodes from other layers. We propose an integrated algorithmic approach for estimating multi-layered networks, that incorporates a screening step for significant variables, an optimization algorithm for estimating the key model parameters and a stability selection step for selecting the most stable effects. The optimization is solved on a reduced parameter space that is identified through screening. Theoretical properties are considered to ensure identifiability and consistent estimation of the parameters and convergence of the optimization algorithm, despite the lack of global convexity. The performance is assesses on synthetic and real data sets.
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