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Abstract:
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Recent work has demonstrated the value of systems featuring tight-knit integration of statistical and computational environments with interactive visualization as part of exploratory data analysis for genomic datasets. In the genomic case, exploration and navigation is usually geared towards data arranged spatially over genomic location. In other applications, specifically in metagenomics, where counts of sequences for bacterial taxonomic units are obtained for a large number so samples, this organization into spatial coordinates is not valid, presenting a challenge for navigation and exploration of these datasets. In this talk we present methods and tools to explore datasets of these type that take advantage of the hierarchical organization of features in these data and discuss how tight-knit integration of computation where summaries and aggregates of these hierarchical domains is used to efficiently explore these datasets in an interactive visualization setting. We conclude by showing how this hierarchical exploration is applicable as well for exploration of hierarchical structure of epigenomes.
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