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Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge
1Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute for Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235;
Summary
Hybrid analytical instrumentation using mass spectrometry (MS) drives big data in science and medicine. New strategies for mining and visualizing these large datasets are crucial for extracting systems-level insights.
Area of Science:
- Analytical Chemistry
- Biomedical Science
- Data Science
Background:
- Mass spectrometry (MS) is central to hybrid analytical instrumentation.
- Multidimensional separations coupled with MS are essential for omics, drug discovery, and synthetic biology.
- These techniques generate large-scale datasets for systems-level biological inference.
Purpose of the Study:
- To describe multidimensional MS configurations as big data technologies.
- To review emerging strategies for mining large-scale datasets.
- To discuss data visualization for high-dimensional data.
Main Methods:
- Review of multidimensional mass spectrometry configurations.
- Analysis of information content from individual and combined data dimensions.
- Exploration of data mining and visualization techniques for large datasets.
Main Results:
- Multidimensional MS generates high peak capacity and throughput, driving big data acquisition.
- Comparing different data levels provides unique insights.
- Emerging strategies enhance information extraction from complex datasets.
Conclusions:
- Multidimensional MS is a key technology for big data in science and medicine.
- Advanced data mining and visualization are critical for interpreting complex MS data.
- These approaches enable deeper understanding of biological systems.
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