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 2018
UL http://biorxiv.org/content/early/2018/02/27/128579.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 who study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases with other data can provide insights into shared genomic variants and shared environmental exposures that may be implicated in such diseases. 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. The main contribution of our design study 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. By designing the genealogy graph layout to align with a tabular view, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We validate our designs by conducting case studies with our domain collaborators.