Multivariate data analysis for metabolomics

Khatib, Alfi (2019) Multivariate data analysis for metabolomics. In: Adjunct Professor Programme 2019, Universistas Airlangga, 28th November 2019, Faculty of Pharmacy, Universistas Airlangga, Surabaya, Indonesia. (Unpublished)

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Abstract

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.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	6915/87536
Uncontrolled Keywords:	Multivariate data analysis, metabolomics, principal component analysis, partial least square, latent variables
Subjects:	Q Science > Q Science (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Pharmacy > Department of Pharmaceutical Chemistry
Depositing User:	Dr. Alfi Khatib
Date Deposited:	12 Jan 2021 14:32
Last Modified:	12 Jan 2021 14:32
URI:	http://irep.iium.edu.my/id/eprint/87536

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