OmicsVis: an interactive tool for visually analyzing metabolomics data

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.

Relational Model Bases: A Technical Approach to Real-time Business Intelligence and Decision Making

“…Therefore, the potential for these relational model base systems includes any decision-making scenario where real-time business intelligence is needed and where there is value in automating the decision making to provide this real-time data, providing a tool with the capability to solve many complex problems in several knowledge domains. These areas include public health and emergency services (i.e., pandemic response plans), bioinformatics, customer segmentation, and inventory management, in addition to the more widely recognized areas of fraud detection, risk management, financial modeling, and trade execution [Kloeckl, Senn and Ratti, 2012;Lederman and Wynter, 2011;Livengood, Maciejewski, Wei and Ebert, 2012;Maciejewski et al, 2011].…”

Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

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