PRONE
This is the development version of PRONE; for the stable release version, see PRONE.
All Bioconductor versions of PRONE
3.24 (devel), 3.23 (release), 3.22, 3.21, 3.20
The PROteomics Normalization Evaluator
Bioconductor version: 3.24 · Package version: 1.7.0
High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.
Maintainer: Lis Arend <lis.arend at tum.de>
Citation
From within R, enter citation("PRONE"):
Lis Arend. PRONE: The PROteomics Normalization Evaluator. doi:10.18129/B9.bioc.PRONE, R package version 1.7.0, https://bioconductor.org/packages/PRONE.
Generated from the package metadata; it may differ from the package's own citation.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## The following initializes the development version of Bioconductor
BiocManager::install(version = "devel")
BiocManager::install("PRONE")For older versions of R, please refer to the appropriate Bioconductor release.
Details
| Version | 1.7.0 |
| License | GPL (>= 3) |
| URL | https://github.com/daisybio/PRONE |
| Bug Reports | https://github.com/daisybio/PRONE/issues |
| Last updated | 2026-04-28 |
| In Bioconductor since | BioC 3.20 (R-4.4) (1 year) |
| Downloads rank | 1615 of 2,456 |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | DifferentialExpression, Normalization, Preprocessing, Proteomics, Software, Visualization |
| Package Short Url | https://bioconductor.org/packages/PRONE/ |
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("PRONE")| Getting started with PRONE | HTML | R Script |
| Preprocessing | HTML | R Script |
| Normalization | HTML | R Script |
| Imputation | HTML | R Script |
| Differential Expression Analysis | HTML | R Script |
| PRONE with Spike-In Data | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Download
Follow the installation instructions to use this package in your R session.
| Source package | PRONE_1.7.0.tar.gz |
| Windows binary (x86_64) | PRONE_1.7.0.zip |
| macOS binary (arm64) | PRONE_1.7.0.tgz |
| macOS binary (x86_64) | PRONE_1.7.0.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/PRONE |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/PRONE |
| Package Downloads Report | Download Stats |
Dependencies
Depends: R (>= 4.4.0), SummarizedExperiment
Imports: dplyr, magrittr, data.table, RColorBrewer, ggplot2, S4Vectors, ComplexHeatmap, stringr, NormalyzerDE, tibble, limma, MASS, edgeR, matrixStats, preprocessCore, stats, gtools, methods, ROTS, ComplexUpset, tidyr, purrr, circlize, gprofiler2, plotROC, MSnbase, UpSetR, dendsort, vsn, Biobase, reshape2, POMA, ggtext, scales, DEqMS, vegan
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, DT