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

Anne-Laure Boulesteix; Korbinian Strimmer

doi:10.1093/bib/bb1016

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

Anne-Laure Boulesteix, Korbinian Strimmer

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	32-44
Number of pages	9
Journal	Briefings in Bioinformatics
Volume	8
Issue number	1
DOIs	https://doi.org/10.1093/bib/bb1016
Publication status	Published - Jan 2007

Keywords

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

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10.1093/bib/bb1016

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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.",

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KW - gene expression

KW - classification

KW - dimension reduction

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

Abstract

Keywords

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