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Extract Metabolomic Information from Mass Spectrometry Images Using Advanced Data Analysis

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Mass Spectrometry Imaging of Small Molecules

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2437))

Abstract

Mass spectrometry imaging (MSI) data generally contains large sizes and high-dimensional structures due to their inherent complex chemical and spatial information. A variety of data analysis methods have been developed to comprehensively analyze the MSI experimental results and extract essential information. Here, we describe the protocols of data preprocessing and emerging methods for data analyses, including multivariate analysis, machine learning, and image fusion, that have been applied to the data generated from the Single-probe MSI technique. These strategies and methods can be potentially applied to handling data produced from other MSI techniques.

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Acknowledgments

This research project is partially supported by grants from National Institutes of Health (R01GM116116), National Science Foundation (OCE-1634630), and Research Council of the University of Oklahoma Norman Campus.

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Author notes

  1. Xiang Tian

    Present address: Dynamic Omics, Center of Genomics Research (CGR), R&D, AstraZeneca, Gaithersburg, MD, USA

  2. Xiang Tian and Zhu Zou contributed equally to this work.

Authors and Affiliations

  1. Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, USA

    Xiang Tian, Zhu Zou & Zhibo Yang

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  1. Xiang Tian
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  2. Zhu Zou
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  3. Zhibo Yang
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Corresponding author

Correspondence to Zhibo Yang .

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Editors and Affiliations

  1. Department of Chemistry, Iowa State University, Ames, IA, USA

    Young-Jin Lee

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Tian, X., Zou, Z., Yang, Z. (2022). Extract Metabolomic Information from Mass Spectrometry Images Using Advanced Data Analysis. In: Lee, YJ. (eds) Mass Spectrometry Imaging of Small Molecules. Methods in Molecular Biology, vol 2437. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2030-4_18

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  • DOI: https://doi.org/10.1007/978-1-0716-2030-4_18

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2029-8

  • Online ISBN: 978-1-0716-2030-4

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