Installation
About
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).
| Citation | promor citation info |
| github.com/caranathunge/promor | |
| caranathunge.github.io/promor/ | |
| Bug report | File report |
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