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normScore

Evaluation and Ranking of Normalization Methods for Proteomics Data

Bioconductor version: 3.24 · Package version: 0.99.1

Provides tools to evaluate and rank normalization methods for omics datasets using a composite score derived from multiple performance metrics. The package is designed to support systematic benchmarking and comparison of normalization strategies across datasets and experimental settings. It also includes utilities for summarizing results and visualizing normalization performance.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("normScore")

Details

MaintainerJulia García Currás <julia.gcurras@udc.es>
AuthorJulia García Currás [aut, cre] (ORCID: <https://orcid.org/0009-0002-6354-5035>), Axencia Galega de Innovación (GAIN), Xunta de Galicia [fnd] (Industrial Doctorate Grant 2022-2026, Ref. 23_IN606D_2022_2707220)
LicenseGPL-2
URLhttps://github.com/juliagcurras/normScore, https://juliagcurras.github.io/normScore/
Bug Reportshttps://https://github.com/juliagcurras/normScore/issues
Source branchdevel
biocViewsMultipleComparison, Normalization, Preprocessing, Proteomics, QualityControl, Software

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packagenormScore_0.99.1.tar.gz
Windows binary (x86_64)normScore_0.99.1.zip
macOS binary (arm64)normScore_0.99.1.tgz
macOS binary (x86_64)normScore_0.99.1.tgz
Dependencies

Depends: R (>= 4.5)

Imports: stats, ggplot2, ggpubr, boot, MASS, rlang, withr

Suggests: knitr, rmarkdown, testthat (>= 3.0.0), limma, NormalyzerDE, SummarizedExperiment, S4Vectors, methods, BiocStyle, BiocManager