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ProteinPCA  

Principal Component Analysis (PCA) Tool on Protein Expression Data
View on CRAN: Click here


Download and install ProteinPCA package within the R console
Install from CRAN:
install.packages("ProteinPCA")

Install from Github:
library("remotes")
install_github("cran/ProteinPCA")

Install by package version:
library("remotes")
install_version("ProteinPCA", "0.1.1")



Attach the package and use:
library("ProteinPCA")
Maintained by
Paul Angelo C. Manlapaz
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-04-12
Latest Update: 2025-04-12
Description:
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>.
How to cite:
Paul Angelo C. Manlapaz (2025). ProteinPCA: Principal Component Analysis (PCA) Tool on Protein Expression Data. R package version 0.1.1, https://cran.r-project.org/web/packages/ProteinPCA. Accessed 24 Oct. 2026.
Previous versions and publish date:
(2026-07-09 08:17), 0.1.0 (2025-04-12 10:30)
Other packages that cited ProteinPCA R package
View ProteinPCA citation profile
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Complete documentation for ProteinPCA
Functions, R codes and Examples using the ProteinPCA R package
Full ProteinPCA package functions and examples
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