Statistical analysis and modeling of mass spectrometry-based metabolomics data

Bowei Xi, Haiwei Gu, Hamid Baniasadi, Daniel Raftery

Research output: Contribution to journalArticlepeer-review

104 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)333-353
Number of pages21
JournalMethods in Molecular Biology
StatePublished - 2014

Bibliographical note

Publisher Copyright:
© Springer Science+Business Media New York 2014.


  • Classification
  • Mass spectrometry
  • Metabolomics
  • Multivariate statistics


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