Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art

Anne-Christin Hauschild, Till Schneider, Josch Pauling, Kathrin Rupp, Mi Jang, Jörg Baumbach, Jan Baumbach
<span title="2012-10-16">2012</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="" style="color: black;">Metabolites</a> </i> &nbsp;
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
more &raquo; ... 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 OPEN ACCESS
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.3390/metabo2040733</a> <a target="_blank" rel="external noopener" href="">pmid:24957760</a> <a target="_blank" rel="external noopener" href="">pmcid:PMC3901238</a> <a target="_blank" rel="external noopener" href="">fatcat:ylfovhqs6rcvvjnr2y3gscb5bq</a> </span>
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