ProteinPCA: Principal Component Analysis (PCA) Tool on Protein Expression Data

Analysis of protein expression data can be done through Principal Component Analysis (PCA), and this R package is designed to streamline the analysis. This package enables users to perform PCA and it generates biplot and scree plot for advanced graphical visualization. Optionally, it supports grouping/clustering visualization with PCA loadings and confidence ellipses. With this R package, researchers can quickly explore complex protein datasets, interpret variance contributions, and visualize sample clustering through intuitive biplots. For more details, see Jolliffe (2001) <doi:10.1007/b98835>, Gabriel (1971) <doi:10.1093/biomet/58.3.453>, Zhang et al. (2024) <doi:10.1038/s41467-024-53239-9>, and Anandan et al. (2022) <doi:10.1038/s41598-022-07781-5>.

Version: 0.1.0
Imports: stats, ggplot2, gridExtra
Suggests: testthat
Published: 2025-04-12
DOI: 10.32614/CRAN.package.ProteinPCA
Author: Paul Angelo C. Manlapaz ORCID iD [aut, cre]
Maintainer: Paul Angelo C. Manlapaz <pacmanlapaz at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: ProteinPCA results

Documentation:

Reference manual: ProteinPCA.pdf

Downloads:

Package source: ProteinPCA_0.1.0.tar.gz
Windows binaries: r-devel: ProteinPCA_0.1.0.zip, r-release: ProteinPCA_0.1.0.zip, r-oldrel: ProteinPCA_0.1.0.zip
macOS binaries: r-release (arm64): ProteinPCA_0.1.0.tgz, r-oldrel (arm64): ProteinPCA_0.1.0.tgz, r-release (x86_64): ProteinPCA_0.1.0.tgz, r-oldrel (x86_64): ProteinPCA_0.1.0.tgz

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