A visual analysis system for metabolomics data

Philip Livengood, Ross Maciejewski, Wei Chen, David S. Ebert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. In this work we present a novel system that enables interactive comparative visualization and analysis of metabolomics data obtained by two-dimensional gas chromatography-mass spectrome-try (GCxGC-MS). Our system allows the user to produce, and interactively explore, visualizations of multiple GCxGC-MS data sets, thereby allowing a user to discover differences and features in real time. Our system provides statistical support in the form of mean and standard deviation calculations to aid users in identifying meaningful differences between sample groups. We combine these with multiform, linked visualizations in order to provide researchers with a powerful new tool for GCxGC-MS exploration and bio-marker discovery.

Original languageEnglish (US)
Title of host publicationIEEE Symposium on Biological Data Visualization 2011, BioVis 2011 - Proceedings
Pages71-78
Number of pages8
DOIs
StatePublished - Dec 28 2011
Event1st IEEE Symposium on Biological Data Visualization, BioVis 2011 - Providence, RI, United States
Duration: Oct 23 2011Oct 24 2011

Publication series

NameIEEE Symposium on Biological Data Visualization 2011, BioVis 2011 - Proceedings

Other

Other1st IEEE Symposium on Biological Data Visualization, BioVis 2011
Country/TerritoryUnited States
CityProvidence, RI
Period10/23/1110/24/11

Keywords

  • GCXGC-MS
  • Metabolomics
  • TIC
  • visual analysis

ASJC Scopus subject areas

  • Biotechnology
  • Computer Vision and Pattern Recognition
  • Information Systems

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