Research prototype visual analysis tool for comparison of patient phenotypes of cross-sectional cohorts, based on the Human Phenotype Ontology
This project is currently under development. The source code provided here should be considered alpha-code for proof of concept and for reference purposes. A documented and modularized re-write is underway and will be posted here when it is available.
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For information, resources, and an online demo of the tool, please visit www.phenostacks.org.
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PhenoStacks uses the Human Phenotype Ontology to structure data for visualization.
PhenoStacks: Cross-Sectional Cohort Phenotype Comparison Visualizations
Michael Glueck, Alina Gvozdik, Fanny Chevalier, Azam Khan, Michael Brudno, Daniel Wigdor
IEEE Transactions on Visualization and Computer Graphics, 2017, In Press
DOI: http://dx.doi.org/10.1109/TVCG.2016.2598469
Cross-sectional phenotype studies are used by genetics researchers to better understand how phenotypes vary across patients with genetic diseases, both within and between cohorts. Analyses within cohorts identify patterns between phenotypes and patients (e.g., co-occurrence) and isolate special cases (e.g., potential outliers). Comparing the variation of phenotypes between two cohorts can help distinguish how different factors affect disease manifestation (e.g., causal genes, age of onset, etc.). PhenoStacks is a novel visual analytics tool that supports the exploration of phenotype variation within and between cross-sectional patient cohorts. By leveraging the semantic hierarchy of the Human Phenotype Ontology, phenotypes are presented in context, can be grouped and clustered, and are summarized via overviews to support the exploration of phenotype distributions. The design of PhenoStacks was motivated by formative interviews with genetics researchers: we distil high-level tasks, present an algorithm for simplifying ontology topologies for visualization, and report the results of a deployment evaluation with four expert genetics researchers. The results suggest that PhenoStacks can help identify phenotype patterns, investigate data quality issues, and inform data collection design.