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This article is part of the supplement: Highlights of the 1st IEEE Symposium on Biological Data Visualization (BioVis 2011)

Open Access Research

OmicsVis: an interactive tool for visually analyzing metabolomics data

Philip Livengood1, Ross Maciejewski2*, Wei Chen3 and David S Ebert1

Author Affiliations

1 Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

2 School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA

3 State Key Lab of CAD & CG, Zhejiang University, China

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BMC Bioinformatics 2012, 13(Suppl 8):S6  doi:10.1186/1471-2105-13-S8-S6

Published: 18 May 2012


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. Due to the size and complexity of the data produced, however, analysis can still be very slow and time consuming. This is further complicated by the fact that datasets obtained will exhibit incidental differences in intensity and retention time, not related to actual chemical differences in the samples being evaluated. Additionally, automated tools to correct these errors do not always produce reliable results. This work presents a new analytics system that enables interactive comparative visualization and analytics of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GC × GC-MS). The key features of this system are the ability to produce visualizations of multiple GC × GC-MS data sets, and to explore those data sets interactively, allowing a user to discover differences and features in real time. The system provides statistical support in the form of difference, standard deviation, and kernel density estimation calculations to aid users in identifying meaningful differences between samples. These are combined with novel transfer functions and multiform, linked visualizations in order to provide researchers with a powerful new tool for GC × GC-MS exploration and bio-marker discovery.