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OmicsVis: an interactive tool for visually analyzing metabolomics data.

Philip Livengood¹, Ross Maciejewski, Wei Chen

¹School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.

BMC Bioinformatics

|May 22, 2012

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Researchers developed a new analytics system for metabolomics data. This tool enables interactive visualization and analysis of two-dimensional gas chromatography-mass spectrometry (GC × GC-MS) data for faster cancer biomarker discovery.

Area of Science:

Metabolomics
Analytical Chemistry
Bioinformatics

Background:

Metabolomics data analysis is crucial for identifying cancer biomarkers by comparing healthy and unhealthy samples.
Data complexity and incidental variations in intensity and retention time slow down analysis and challenge biomarker discovery.
Existing automated error correction tools for metabolomics data lack consistent reliability.

Purpose of the Study:

To present a novel analytics system for interactive comparative visualization and analysis of two-dimensional gas chromatography-mass spectrometry (GC × GC-MS) data.
To enhance the speed and efficiency of exploring complex metabolomics datasets for biomarker identification.
To provide researchers with an advanced tool for real-time discovery of meaningful differences and features in biological samples.

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Main Methods:

Development of an interactive analytics system for GC × GC-MS data.
Implementation of multiform, linked visualizations and novel transfer functions.
Integration of statistical support including difference, standard deviation, and kernel density estimation calculations.

Main Results:

The system enables interactive visualization and exploration of multiple GC × GC-MS datasets in real time.
It facilitates the discovery of differences and features within complex metabolomics data.
Statistical calculations aid in identifying meaningful biological variations between samples.

Conclusions:

The new analytics system offers a powerful tool for GC × GC-MS data exploration.
It significantly aids researchers in the discovery of cancer biomarkers.
Interactive visualization and integrated statistical analysis improve the efficiency of metabolomics research.