Multivariate data analysis for metabolomics - Khatib, Alfi (2019)
Statistical calculation implying multi-variables often lead to the statistical errors. It can be concealed by applying multivariate data analysis (MDA). In this lecture, the application of MDA to metabolomics is discussed. MDA assists correlation of multiple parameters in metabolomics. Different type of MDA is discussed including principal component analysis, partial least square, orthogonal-partial least square, and partial least square-discriminant analysis. Different vaidation techniques are also described such as model and variable validiation, and permutation. Finally, the fundamental principle of latent variable is discussed in relation to score and loading plots.