Statistical Analysis and Modeling of Mass Spectrometry-Based Metabolomics Data

  • Bowei Xi
  • Haiwei Gu
  • Hamid Baniasadi
  • Daniel Raftery
Part of the Methods in Molecular Biology book series (MIMB, volume 1198)


Multivariate statistical techniques are used extensively in metabolomics studies, ranging from biomarker selection to model building and validation. Two model independent variable selection techniques, principal component analysis and two sample t-tests are discussed in this chapter, as well as classification and regression models and model related variable selection techniques, including partial least squares, logistic regression, support vector machine, and random forest. Model evaluation and validation methods, such as leave-one-out cross-validation, Monte Carlo cross-validation, and receiver operating characteristic analysis, are introduced with an emphasis to avoid over-fitting the data. The advantages and the limitations of the statistical techniques are also discussed in this chapter.

Key words

Metabolomics Mass spectrometry Multivariate statistics Classification 



This article was written while one of the authors, Bowei Xi, was on sabbatical leave at the Statistical and Applied Mathematical Sciences Institute (SAMSI, Research Triangle Park, NC). This work is partially funded by NSF DMS-1228348, ARO W911NF-12-1-0558, DoD MURI W911NF-08-1-0238 (BX) and NIH R01GM085291 (DR).


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Bowei Xi
    • 1
  • Haiwei Gu
    • 2
    • 3
  • Hamid Baniasadi
    • 4
  • Daniel Raftery
    • 2
    • 5
  1. 1.Department of StatisticsPurdue UniversityWest LafayetteUSA
  2. 2.Department of Anesthesiology & Pain Medicine, Northwest Metabolomics Research CenterUniversity of WashingtonSeattleUSA
  3. 3.Jiangxi Key Laboratory for Mass Spectrometry and InstrumentationEast China Institute of TechnologyNanchang, JiangxiChina
  4. 4.Department of ChemistryPurdue UniversityWest LafayetteUSA
  5. 5.Fred Hutchinson Cancer Research CenterSeattleUSA

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