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Bioinformatics Advance Access originally published online on March 7, 2007
Bioinformatics 2007 23(9):1164-1167; doi:10.1093/bioinformatics/btm069
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

pcaMethods—a bioconductor package providing PCA methods for incomplete data

Wolfram Stacklies 1, Henning Redestig 2, Matthias Scholz 3, Dirk Walther 2 and Joachim Selbig 2,4,*

1CAS-MPG Partner Institute for Comp. Biology, 320 Yue Yang Road, 200031 Shanghai, China, 2Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Golm, 3Ernst-Moritz-Arndt-University of Greifswald, Competence Center for Functional Genomics, F.L.Jahnstraße 15, 17487, Greifswald and 4University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany

*To whom correspondence should be addressed.


  Abstract

Summary: 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.

Availability: http://www.bioconductor.org

Contact: selbig{at}mpimp-golm.mpg.de

Supplementary information: Please visit our webpage at http://bioinformatics.mpimp-golm.mpg.de/

Associate Editor: Olga Troyanskaya

Received on October 25, 2006; revised on February 13, 2007; accepted on February 21, 2007

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