Analysis of multiblock datasets using ComDim: Overview and extension to the analysis of (K + 1) datasets

• Published in: Journal of chemometrics 2016-08, Vol.30 (8), p.420-429
• Main Authors: El Ghaziri, Angélina, Cariou, Véronique, Rutledge, Douglas N., Qannari, El Mostafa
• Format: Article
• Language: English
• Subjects: Case studies; Chemometrics; ComDim; Datasets; Hierarchical Principal Component Analysis; Illustrations; Least squares method; multiblock datasets; multiblock partial least squares regression; NMR; Nuclear magnetic resonance; Regression; Regression analysis; Statistics; Strategy; Time domain
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/cem.2810

ComDim analysis was designed to assess the relationships between individuals and variables within a multiblock setting where several variables, organized in blocks, are measured on the same individuals. An overview of this method is presented together with some of its properties. Furthermore, we discuss a new extension of the method of analysis to the case of (K+1) datasets. More precisely, the aim is to explore the relationships between a response dataset and K other datasets. An illustration of this latter strategy of analysis on the basis of a case study involving Time Domain ‐ Nuclear Magnetic Resonance data is outlined and the outcomes are compared with those of Multiblock Partial Least Squares regression. An overview of ComDim analysis is presented together with some of its properties. Furthermore, a new extension of this method to the case of K+1 datasets is discussed. More precisely, the aim is to explore the relationships between a response dataset and K other datasets. An illustration of this latter strategy of analysis on the basis of Time Domain ‐ Nuclear Magnetic Resonance data is outlined and the outcomes are compared to those of Multiblock PLS regression.