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Abstract:
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Current work for constructing graphical models for multivariate data does not take into account the subject specific information, which can bias the conditional independence structure in heterogeneous data. In cancer genomic studies, tumor samples are inherently heterogeneous with contaminated mixtures of normal and tumor cells. Ignoring the cellular heterogeneity in tumors and modeling the population-level genomic graphs, may inhibit the discovery of the true tumor graph, which would be attenuated towards the normal graph. We propose a novel edge regression model for undirected graphs, which incorporates subject-level covariates to estimate the conditional dependencies. Our model allows undirected networks to vary with the exogenous covariates and is able to borrow strength from different related graphs for estimating more robust covariate-specific graphs. Bayesian shrinkage algorithms are presented to efficiently estimate and induce sparsity for generating subject-level graphs. We demonstrate the performance of our method through simulation studies. We apply our method to cytokine measurements from blood plasma samples from hepatocellular carcinoma patients and normal controls.
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