Sparse Bayesian dynamic network models, with genomics applications
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Network models have become an important topic in modern statistics, and the evolution of network structure over time is an important new area of study, relevant to a range of applications. An important application of statistical network modelling is in genomics: network models are a natural way to describe and analyse patterns of interactions between genes and their products. However, whilst network models are well established in genomics, historically these models have mostly been static network models, ignoring the dynamic nature of genomic processes. In this work, we propose a model to infer dynamic genomic network structure, based on single-cell measurements of gene-expression counts. Our model draws on ideas from the Bayesian lasso and from copula modelling, and is implemented efficiently by combining Gibbs- and slice-sampling techniques. We apply the modelling to data from neural development, and infer changes in network structure which match current biological knowledge, as well as discovering novel network structures which identify potential targets for further experimental investigation by neuro-biologists.
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