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Abstract:
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Identifying and characterizing patterns of association between variables is a common aim in biology today. Studying these associations has played a crucial role in understanding a wide variety of biological phenomena, such as the dynamics of human disease, transcriptional changes associated with aging, or condition-specific alterations to metabolic pathways. In a statistical framework, these associations between variables are commonly described in terms of precision matrices (which encode conditional associations) or covariance matrices (which capture marginal associations). We present an extension of our already presented method to model the structure of related covariance matrices which captures both common structural elements across all conditions and condition specific differences in associations between variables. Specifically, we extend our method to time series data. We will present the theoretical framework for this model and a method for visualizing the results. We will illustrate the model on a set of time course metabolomics data from an experiment to study the molecular determinants of mitochondrial fuel selection, exercise capacity and metabolic health in humans.
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