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
| Maintainer | Julia García Currás <julia.gcurras@udc.es> |
| Author | Julia 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) |
| License | GPL-2 |
| URL | https://github.com/juliagcurras/normScore, https://juliagcurras.github.io/normScore/ |
| Bug Reports | https://https://github.com/juliagcurras/normScore/issues |
| Source branch | devel |
| biocViews | MultipleComparison, Normalization, Preprocessing, Proteomics, QualityControl, Software |
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | normScore_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