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. 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.
Highlights
In recent years, GCxGC-MS has become an invaluable laboratory analysis tool
While statistical analysis has played an important role in this work, the large data size, inherent biological variability and measurement noise makes the identification of biomarkers through purely statistical processes extremely difficult and time consuming
This work presents a visual analytics environment for analysis and visualization of metabolomic data obtained by GCxGC-MS
Summary
GCxGC-MS has become an invaluable laboratory analysis tool. this procedure produces large (gigabytes of data per sample), four dimensional datasets (retention time one, retention time two, mass and intensity). The technique is similar to rendering a normal mass spectrum In this case, the zero intensity baseline is drawn across the middle of the viewing window, with bars for positive differences rising upward, and bars for Figure 3 Color mapping examples. Three-dimensional mass spectrum exploration We can explore data in a three-dimensional visual representation to depict its properties such as the location, the uncertainty and the weight
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