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This research uses visual analytics to better understand genome dynamics, through a novel application of topological data analysis (TDA) to time series gene expression data. TDA is a model-free approach in which relations are obtained directly from the data. We build the dynamics of the system into the topology, then calculate the influence of potential regulatory genes over the expression of other genes. An interactive 3D visualization is provided to aid in the discovery of functional relationships. These capabilities are contained in a new R package. We apply our technique to synthetic data from the DREAM4 gene regulatory network inference challenge and compare our results to both the challenge submissions and those produced by networkBMA, a Bioconductor package designed to work with time series gene expression data. A case study is presented detailing the use of the visual analytics tool.

The Centers for Medicare and Medicaid Services (CMS) has made public a data set showing what hospitals charged and what Medicare paid for the one hundred most common inpatient stays. Here we present the application of Reduced Basis Decomposition (RBD), an efficient novel dimension reduction algorithm for data processing, to the CMS data. This was paired with a comparative visual exploration of the results when put into context with characteristics of the hospitals and marketplaces in which they operate. We used Weave Analyst, a new web-based analysis and visualization environment, to visualize the relationship between the hospital groups, their charge levels, and distinguishing indicator variables. Particular insights to the relatively small number of underlying factors that exert greatest influence on hospital pricing surfaced thanks to the combined synergetic integration of the modeling, reduction, and visualization techniques.