Statistical analysis and modeling of mass spectrometry-based metabolomics data.
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.
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