Partial Least Squares Regression Models for the Analysis of Kinase Signaling

Danielle L Bourgeois; Pamela K Kreeger

doi:10.1007/978-1-4939-7154-1_32

Partial Least Squares Regression Models for the Analysis of Kinase Signaling

Methods Mol Biol. 2017:1636:523-533. doi: 10.1007/978-1-4939-7154-1_32.

Authors

Danielle L Bourgeois¹, Pamela K Kreeger²

Affiliations

¹ Department of Biomedical Engineering, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI, 53705, USA.
² Department of Biomedical Engineering, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI, 53705, USA. kreeger@wisc.edu.

PMID: 28730500
DOI: 10.1007/978-1-4939-7154-1_32

Abstract

Partial least squares regression (PLSR) is a data-driven modeling approach that can be used to analyze multivariate relationships between kinase networks and cellular decisions or patient outcomes. In PLSR, a linear model relating an X matrix of dependent variables and a Y matrix of independent variables is generated by extracting the factors with the strongest covariation. While the identified relationship is correlative, PLSR models can be used to generate quantitative predictions for new conditions or perturbations to the network, allowing for mechanisms to be identified. This chapter will provide a brief explanation of PLSR and provide an instructive example to demonstrate the use of PLSR to analyze kinase signaling.

Keywords: Data-driven modeling; PLSR; Proteomics; Regression analysis; Systems biology.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Computational Biology / methods
Least-Squares Analysis*
Linear Models*
Protein Kinases / metabolism*
Regression Analysis*
Reproducibility of Results
Signal Transduction*

Substances

Protein Kinases

Abstract

Publication types

MeSH terms

Substances

Grants and funding