Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs

Carolina Nobre, View ORCID ProfileNils Gehlenborg, View ORCID ProfileHilary Coon, View ORCID ProfileAlexander Lex
doi: https://doi.org/10.1101/128579
Carolina Nobre
•Carolina Nobre and Alexander Lex are with the University of Utah. E-mail: ,
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: cnobre@sci.utah.edu alex@sci.utah.edu
Nils Gehlenborg
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Nils Gehlenborg
Hilary Coon
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Hilary Coon
Alexander Lex
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Alexander Lex
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted February 27, 2018.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs
Carolina Nobre, Nils Gehlenborg, Hilary Coon, Alexander Lex
bioRxiv 128579; doi: https://doi.org/10.1101/128579
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs
Carolina Nobre, Nils Gehlenborg, Hilary Coon, Alexander Lex
bioRxiv 128579; doi: https://doi.org/10.1101/128579

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4384)
  • Biochemistry (9609)
  • Bioengineering (7103)
  • Bioinformatics (24896)
  • Biophysics (12630)
  • Cancer Biology (9972)
  • Cell Biology (14365)
  • Clinical Trials (138)
  • Developmental Biology (7966)
  • Ecology (12124)
  • Epidemiology (2067)
  • Evolutionary Biology (16001)
  • Genetics (10936)
  • Genomics (14754)
  • Immunology (9880)
  • Microbiology (23697)
  • Molecular Biology (9489)
  • Neuroscience (50924)
  • Paleontology (370)
  • Pathology (1541)
  • Pharmacology and Toxicology (2686)
  • Physiology (4022)
  • Plant Biology (8672)
  • Scientific Communication and Education (1511)
  • Synthetic Biology (2402)
  • Systems Biology (6444)
  • Zoology (1346)