Partial least squares: a versatile tool for the analysis of high-dimensional genomic data
Partial least squares: a versatile tool for the analysis of high-dimensional genomic data
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DOI：
10.1093/bib/bb1016
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发表时间：
2007-01-01
影响因子：
9.5
通讯作者：
Strimmer, Korbinian
中科院分区：
文献类型：
--
作者：
Boulesteix, Anne-Laure;Strimmer, Korbinian
偏最小二乘 (PLS) 是一种有效的统计回归技术，非常适合基因组和蛋白质组数据的分析。在本文中，我们回顾了 PLS 的基础理论以及 PLS 的许多生物信息学应用。特别是，我们对当前使用的 PLS 方法进行了系统比较，并讨论了各种分析问题，例如：根据转录组数据对肿瘤进行分类、相关基因的识别、生存分析以及基因网络和转录因子活性的建模。
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.