pcaMethods—a bioconductor package providing PCA methods for incomplete data
University of Potsdam · Universität Greifswald
Abstract
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@mpimp-golm.mpg.de Supplementary information: Please visit our webpage at http://bioinformatics.mpimp-golm.mpg.de/
Citation impact
- FWCI
- 2.96
- Percentile
- 100%
- References
- 16
Authors
5- WSWolfram Stacklies
University of Potsdam, Universität Greifswald
- HRHenning Redestig
University of Potsdam, Universität Greifswald
- MSMatthias Scholz
University of Potsdam, Universität Greifswald
- DWDirk Walther
University of Potsdam, Universität Greifswald
- JSJoachim SelbigCorresponding
University of Potsdam, Universität Greifswald
Topics & keywords
- Bioconductor
- Computer science
- Missing data
- Principal component analysis
- Set (abstract data type)
- Data mining
- Data set
- R package