|Title||Approaches to sample size determination for multivariate data : Applications to PCA and PLS-DA of omics data|
|Author(s)||Saccenti, Edoardo; Timmerman, Marieke E.|
|Source||Journal of Proteome Research 15 (2016)8. - ISSN 1535-3893 - p. 2379 - 2393.|
Systems and Synthetic Biology
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||covariance estimation - dimensionality - eigenvalue distribution - hypothesis testing - loading estimation - multivariate analysis - power analysis - random matrix theory|
Sample size determination is a fundamental step in the design of experiments. Methods for sample size determination are abundant for univariate analysis methods, but scarce in the multivariate case. Omics data are multivariate in nature and are commonly investigated using multivariate statistical methods, such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). No simple approaches to sample size determination exist for PCA and PLS-DA. In this paper we will introduce important concepts and offer strategies for (minimally) required sample size estimation when planning experiments to be analyzed using PCA and/or PLS-DA.