title: Statistical analysis and modeling of mass spectrometry-based metabolomics data.
abstract: 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.
authors.affiliation: Department of Statistics, Purdue University, 250 North University Street, West Lafayette, IN, 47907, USA, xbw@purdue.edu.
authors.forename: Bowei
authors.initials: B
authors.lastname: Xi
authors.forename: Haiwei
authors.initials: H
authors.lastname: Gu
authors.forename: Hamid
authors.initials: H
authors.lastname: Baniasadi
authors.forename: Daniel
authors.initials: D
authors.lastname: Raftery
mesh_terms: D016208:Databases, Factual; D016002:Discriminant Analysis; D006801:Humans; D016018:Least-Squares Analysis; D016015:Logistic Models; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D060388:Support Vector Machine