promor: Proteomics Data Analysis and Modeling Tools
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: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
reshape2, ggplot2, ggrepel, gridExtra, limma, statmod, pcaMethods, VIM, missForest, caret, kernlab, xgboost, viridis, pROC |
| Suggests: |
covr, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2022-07-20 |
| Author: |
Chathurani Ranathunge
 [aut, cre,
cph] |
| Maintainer: |
Chathurani Ranathunge <caranathunge86 at gmail.com> |
| 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 |
| Materials: |
README NEWS |
| CRAN checks: |
promor results |
Documentation:
Downloads:
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