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Megavariate Analysis of Environmental QSAR Data. Part II – Investigating Very Complex Problem Formulations Using Hierarchical, Non-Linear and Batch-Wise Extensions of PCA and PLS

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Summary

Three extensions of the basic PCA and PLS methodologies are described. These extensions are hierarchical, non-linear and batch-based in nature. The objectives of these methods are to assist in problem understanding and problem solving in very complex (QSAR) problem formulations. The method extensions are illustrated using two example QSAR data sets containing many X- and Y-variables.

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Abbreviations

ACE:

alternating conditional expectations

BIF-PLS:

bifocal PLS

MARS:

multivariate adaptive regression splines

MLR:

multiple linear regression

NPLS:

non-linear PLS

NN:

neural networks

PARAFAC:

parallel factor analysis

PCA:

principal component analysis

PCB:

polychlorinated biphenyls

PCR:

principal component regression

PLS:

partial least squares projections to latent structures

OPLS:

orthogonal PLS

PLS-DA:

PLS discriminant analysis

QSAR:

quantitative structure-activity relationships

SIMCA:

soft independent modelling of class analogy

SMD:

statistical molecular design

SPLS:

spline PLS

SVM:

support vector machines

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Authors and Affiliations

  1. Umetrics AB, POB 7960, S-907 19, Umeå, Sweden

    Lennart Eriksson & Erik Johansson

  2. Institute of Environmental Chemistry, Department of Chemistry, Umeå University, S-901 87, Umeå, Sweden

    Patrik L. Andersson & Mats Tysklind

Authors
  1. Lennart Eriksson
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  2. Patrik L. Andersson
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  3. Erik Johansson
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  4. Mats Tysklind
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Correspondence to Lennart Eriksson.

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Eriksson, L., Andersson, P.L., Johansson, E. et al. Megavariate Analysis of Environmental QSAR Data. Part II – Investigating Very Complex Problem Formulations Using Hierarchical, Non-Linear and Batch-Wise Extensions of PCA and PLS. Mol Divers 10, 187–205 (2006). https://doi.org/10.1007/s11030-006-9026-4

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