Extract Metabolomic Information from Mass Spectrometry Images Using Advanced Data Analysis

Methods Mol Biol. 2022:2437:253-272. doi: 10.1007/978-1-0716-2030-4_18.

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.

Keywords: Image fusion; Machine learning; Multivariate data analysis; Single-probe mass spectrometry imaging.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Data Analysis*
  • Machine Learning
  • Mass Spectrometry
  • Metabolomics*
  • Plant Extracts
  • Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization

Substances

  • Plant Extracts