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Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art
Computational Systems Biology Group, Max Planck Institute for Informatics, D-66123, Saarbrücken, Germany
Cluster of Excellence for Multimodal Computing and Interaction,Saarland University, D-66123 Saarbrücken, Germany
Department Microfluidics and Clinical Diagnostics, KIST Europe-Korea Institute of Science and Technology Europe, Campus E7.1, D-66123, Saarbrücken, Germany
Computational Biology group, Department of Mathematics and Computer Science, University of Southern Denmark, DK-5230, Odense, Denmark
* Authors to whom correspondence should be addressed.
Received: 8 August 2012; in revised form: 24 September 2012 / Accepted: 25 September 2012 / Published: 16 October 2012
Abstract: Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.
Keywords: ion mobility spectrometry; clinical diagnostics; peak detection; statistics; statistical learning methods; metabolomics; volatile organic compounds
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MDPI and ACS Style
Hauschild, A.-C.; Schneider, T.; Pauling, J.; Rupp, K.; Jang, M.; Baumbach, J.I.; Baumbach, J. Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art. Metabolites 2012, 2, 733-755.
Hauschild A-C, Schneider T, Pauling J, Rupp K, Jang M, Baumbach JI, Baumbach J. Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art. Metabolites. 2012; 2(4):733-755.
Hauschild, Anne-Christin; Schneider, Till; Pauling, Josch; Rupp, Kathrin; Jang, Mi; Baumbach, Jörg I.; Baumbach, Jan. 2012. "Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art." Metabolites 2, no. 4: 733-755.