RT Journal Article
SR Electronic
T1 Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 128579
DO 10.1101/128579
A1 Carolina Nobre
A1 Nils Gehlenborg
A1 Hilary Coon
A1 Alexander Lex
YR 2017
UL http://biorxiv.org/content/early/2017/04/19/128579.abstract
AB The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper, we introduce Lineage, a novel visual analysis tool, designed to support domain experts that study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases can provide insights into shared genomic variants that could be implicated in diseases, but also into shared environmental exposures. We introduce a data and task abstraction and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. Our main contribution is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families with hundreds of members across several generations. By designing the genealogy graph layout to align with a tabular view that displays clinical data for each family member, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We also discuss how the principles of our methodology can be generalized to other scenarios. We validate our designs using an illustrative example based on real-world data, and report of feedback from domain experts.