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Briefings in Bioinformatics Advance Access published online on May 26, 2006
Briefings in Bioinformatics, doi:10.1093/bib/bbl016
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© The Author 2006. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

Original Papers

Partial least squares: a versatile tool for the analysis of high-dimensional genomic data

Anne-Laure Boulesteix * and Korbinian Strimmer

* To whom correspondence should be addressed.
Anne-Laure Boulesteix, E-mail: anne-laure.boulesteix{at}tum.de


  Abstract

Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as, e.g. tumor classification from transcriptome data, identification of relevant genes, survival analysis and modeling of gene networks and transcription factor activities.

Keywords: partial least squares (PLS); high-dimensional genomic data; gene expression; classification; dimension reduction.

Anne-Laure Boulesteix is a post-doctoral researcher and consultant in biostatistics at the Technical University of Munich. She received her PhD in statistics in 2005 from the University of Munich, and is generally interested in computational statistics and high-dimensional multivariate data analysis.

Korbinian Strimmer is heading the ‘Information Theory and Bioinformatics’ group at the Department of Statistics of the University of Munich. His research focuses on statistical learning procedures, complex networks and statistical genomics.


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