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Statistical analysis and modeling of mass spectrometry-based metabolomics data.
Xi, Bowei; Gu, Haiwei; Baniasadi, Hamid; Raftery, Daniel.
Afiliação
  • Xi B; Department of Statistics, Purdue University, 250 North University Street, West Lafayette, IN, 47907, USA, xbw@purdue.edu.
Methods Mol Biol ; 1198: 333-53, 2014.
Article em En | MEDLINE | ID: mdl-25270940
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Espectrometria de Massas / Modelos Estatísticos / Metabolômica Tipo de estudo: Health_economic_evaluation / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Methods Mol Biol Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2014 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Espectrometria de Massas / Modelos Estatísticos / Metabolômica Tipo de estudo: Health_economic_evaluation / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Methods Mol Biol Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2014 Tipo de documento: Article