pcaMethods--a bioconductor package providing PCA methods for incomplete data

Wolfram Stacklies; Henning Redestig; Matthias Scholz; Dirk Walther; Joachim Selbig

doi:10.1093/bioinformatics/btm069

pcaMethods--a bioconductor package providing PCA methods for incomplete data

Bioinformatics. 2007 May 1;23(9):1164-7. doi: 10.1093/bioinformatics/btm069. Epub 2007 Mar 7.

Authors

Wolfram Stacklies¹, Henning Redestig, Matthias Scholz, Dirk Walther, Joachim Selbig

Affiliation

¹ CAS-MPG Partner Institute for Comp. Biology, Shanghai, China.

PMID: 17344241
DOI: 10.1093/bioinformatics/btm069

Abstract

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

Publication types

Evaluation Study
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Databases, Genetic*
Gene Expression Profiling / methods*
Information Storage and Retrieval / methods
Models, Genetic*
Models, Statistical
Oligonucleotide Array Sequence Analysis / methods*
Principal Component Analysis*
Sample Size
Software*