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This is the development version of proDA; for the stable release version, see proDA.

Differential Abundance Analysis of Label-Free Mass Spectrometry Data

Bioconductor version: Development (3.20)

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.

Author: Constantin Ahlmann-Eltze [aut, cre] , Simon Anders [ths]

Maintainer: Constantin Ahlmann-Eltze <artjom31415 at>

Citation (from within R, enter citation("proDA")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

Data Import HTML R Script
Introduction HTML R Script
Reference Manual PDF


biocViews Bayesian, DifferentialExpression, MassSpectrometry, Normalization, Proteomics, QualityControl, Regression, Software
Version 1.19.0
In Bioconductor since BioC 3.10 (R-3.6) (4.5 years)
License GPL-3
Imports stats, utils, methods, BiocGenerics, SummarizedExperiment, S4Vectors, extraDistr
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Suggests testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr, tibble, limma, DEP, numDeriv, pheatmap, knitr, rmarkdown, BiocStyle
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Depends On Me
Imports Me MatrixQCvis
Suggests Me protti
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Follow Installation instructions to use this package in your R session.

Source Package proDA_1.19.0.tar.gz
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macOS Binary (x86_64) proDA_1.19.0.tgz
macOS Binary (arm64) proDA_1.19.0.tgz
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