Stacklies Wolfram, Redestig Henning, Scholz Matthias, Walther Dirk, Selbig Joachim
CAS-MPG Partner Institute for Comp. Biology, Shanghai, China.
Bioinformatics. 2007 May 1;23(9):1164-7. doi: 10.1093/bioinformatics/btm069. Epub 2007 Mar 7.
pcaMethods is a Bioconductor compliant library for computing principal component analysis (PCA) on incomplete data sets. The results can be analyzed directly or used to estimate missing values to enable the use of missing value sensitive statistical methods. The package was mainly developed with microarray and metabolite data sets in mind, but can be applied to any other incomplete data set as well.
pcaMethods是一个符合Bioconductor标准的库，用于对不完整数据集进行主成分分析（PCA）。结果可直接进行分析，或用于估计缺失值，以便能够使用对缺失值敏感的统计方法。该软件包主要是针对微阵列和代谢物数据集开发的，但也可应用于任何其他不完整数据集。
