proDA: Differential Abundance Analysis of Label-Free Mass Spectrometry Data

Account for missing values in label-free 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

AuthorConstantin Ahlmann-Eltze [aut, cre] (<>), Simon Anders [ths] (<>)
Bioconductor views Bayesian DifferentialExpression MassSpectrometry Normalization Proteomics QualityControl Regression Software
MaintainerConstantin Ahlmann-Eltze <>
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))


Try the proDA package in your browser

Any scripts or data that you put into this service are public.

proDA documentation built on Nov. 8, 2020, 5:01 p.m.