title: Partial least squares: a versatile tool for the analysis of high-dimensional genomic data.
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.
authors.affiliation: Department of Medical Statistics and Epidemiology, Technical University of Munich, Ismaningerstrasse 22, D-81675 Munich, Germany. anne-laure.boulesteix@tum.de
authors.forename: Anne-Laure
authors.initials: AL
authors.lastname: Boulesteix
authors.forename: Korbinian
authors.initials: K
authors.lastname: Strimmer
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D023281:Genomics / Q000706:statistics & numerical data*; D006801:Humans; D016018:Least-Squares Analysis; D015233:Models, Statistical; D015999:Multivariate Analysis; D012984:Software; D016019:Survival Analysis