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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>).
Getting started
Browse package contents
Package details |
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| Author | Chathurani Ranathunge [aut, cre, cph] (<https://orcid.org/0000-0003-1901-2119>) |
| Maintainer | Chathurani Ranathunge <caranathunge86@gmail.com> |
| License | LGPL (>= 2.1) |
| Version | 0.2.1 |
| URL | https://github.com/caranathunge/promor https://caranathunge.github.io/promor/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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