pcaMethods—a bioconductor package providing PCA methods for incomplete data

Clicks: 8
ID: 297360
2007
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #141 of 825 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 825 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
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/
Reference Key
openalex_W2167942713 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wolfram Stacklies, Henning Redestig, Matthias Scholz, Dirk Walther, Joachim Selbig
Journal BMC Bioinformatics
Year 2007
DOI
10.1093/bioinformatics/btm069
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.