doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie et. al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling (Kuhn (2008) <doi:10.18637/jss.v028.i05>).">

promor: Proteomics Data Analysis and Modeling Tools (original) (raw)

A comprehensive, user-friendly package for label-free proteomics data analysis and machine learning-based modeling. Data generated from 'MaxQuant' can be easily used to conduct differential expression analysis, build predictive models with top protein candidates, and assess model performance. promor includes a suite of tools for quality control, visualization, missing data imputation (Lazar et. al. (2016) <doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie et. al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling (Kuhn (2008) <doi:10.18637/jss.v028.i05>).

Version: 0.2.1
Depends: R (≥ 3.5.0)
Imports: reshape2, ggplot2, ggrepel, gridExtra, limma, statmod, pcaMethods, VIM, missForest, caret, kernlab, xgboost, naivebayes, viridis, pROC
Suggests: covr, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-07-17
DOI: 10.32614/CRAN.package.promor
Author: Chathurani RanathungeORCID iD [aut, cre, cph]
Maintainer: Chathurani Ranathunge
BugReports: https://github.com/caranathunge/promor/issues
License: LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2.1)]
URL: https://github.com/caranathunge/promor,https://caranathunge.github.io/promor/
NeedsCompilation: no
Language: en-US
Citation: promor citation info
Materials: README NEWS
CRAN checks: promor results

Documentation:

Downloads:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=promorto link to this page.