Abstract
Experimental designs in modern experiments in the life sciences often comprise multiple factors. Usually, the number of response variables vastly exceeds the sample size and well-established methods for analysis of multifactor data such as multivariate analysis of variance (MANOVA) cannot be applied. This issue can be circumvented by combining MANOVA with a shrinkage estimator of the correlation matrix. This chapter discusses the principles of this regularized MANOVA (rMANOVA) approach. Two examples from untargeted metabolomics studies are used to demonstrate application of rMANOVA in practice. Focus lies on identification of significant main and interaction effects and the variables associated to them
Original language | English |
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Title of host publication | Comprehensive Chemometrics |
Subtitle of host publication | Chemical and Biochemical Data Analysis |
Editors | Steven Brown, Romà Tauler, Beata Walczak |
Publisher | Elsevier |
Chapter | 1.18 |
Pages | 479-494 |
Volume | 1 |
Edition | 2nd |
ISBN (Print) | 9780444641663 |
DOIs | |
Publication status | Published - 2 Jun 2020 |