TY - JOUR
T1 - OmicsVis
T2 - an interactive tool for visually analyzing metabolomics data.
AU - Livengood, Philip
AU - Maciejewski, Ross
AU - Chen, Wei
AU - Ebert, David S.
N1 - Funding Information:
The Cancer Care Engineering project is supported by the Department of Defense, Congressionally Directed Medical Research Program, Fort Detrick, MD (W81-XWH-08-1-0065) and the Regenstrief Cancer Foundation administered jointly through the Oncological Sciences Center at Purdue University and the Indiana University Simon Cancer Center. This work is supported by the U.S. Department of Homeland Security’s VACCINE Center under Award Number 2009-ST-061-CI0001 and under the 973 program of China (2010CB732504), NSFC 60873123, and NSF of Zhejiang Province (N0. Y1080618). Thanks to Amber Jannasch and Bruce Cooper for their input, feedback, data sets. This article has been published as part of BMC Bioinformatics Volume 13 Supplement 8, 2012: Highlights of the 1st IEEE Symposium on Biological Data Visualization (BioVis 2011). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/ supplements/13/S8.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
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U2 - 10.1186/1471-2105-13-s8-s6
DO - 10.1186/1471-2105-13-s8-s6
M3 - Article
C2 - 22607515
AN - SCOPUS:84871936695
SN - 1471-2105
VL - 13 Suppl 8
SP - S6
JO - BMC bioinformatics
JF - BMC bioinformatics
ER -