Account for missing values in labelfree mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins.
Package details 


Bioconductor views  Bayesian DifferentialExpression MassSpectrometry Normalization Proteomics QualityControl Regression Software 
Maintainer  
License  GPL3 
Version  1.3.1 
URL  https://github.com/constae/proDA 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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