PRONE
This is the development version of PRONE; for the stable release version, see PRONE.
The PROteomics Normalization Evaluator
Bioconductor version: Development (3.21)
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.
Author: Lis Arend [aut, cre] (ORCID:
Maintainer: Lis Arend <lis.arend at tum.de>
citation("PRONE")):
Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("PRONE")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("PRONE")| 1. Getting started with PRONE | HTML | R Script |
| 2. Preprocessing | HTML | R Script |
| 3. Normalization | HTML | R Script |
| 4. Imputation | HTML | R Script |
| 5. Differential Expression Analysis | HTML | R Script |
| 6. PRONE with Spike-In Data | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | DifferentialExpression, Normalization, Preprocessing, Proteomics, Software, Visualization |
| Version | 1.1.0 |
| In Bioconductor since | BioC 3.20 (R-4.4) (< 6 months) |
| License | GPL (>= 3) |
| 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 |
| System Requirements | |
| URL | https://github.com/lisiarend/PRONE |
| Bug Reports | https://github.com/lisiarend/PRONE/issues |
See More
| Suggests | testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, DT |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | PRONE_1.1.0.tar.gz |
| Windows Binary (x86_64) | PRONE_1.1.0.zip (64-bit only) |
| macOS Binary (x86_64) | PRONE_1.1.0.tgz |
| macOS Binary (arm64) | PRONE_1.1.0.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/PRONE |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/PRONE |
| Bioc Package Browser | https://code.bioconductor.org/browse/PRONE/ |
| Package Short Url | https://bioconductor.org/packages/PRONE/ |
| Package Downloads Report | Download Stats |