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Differential Abundance Analysis of Label-Free Mass Spectrometry Data

Bioconductor version: Release (3.19)

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))


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 1.18.0
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_1.18.0.tar.gz
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macOS Binary (x86_64) proDA_1.18.0.tgz
macOS Binary (arm64) proDA_1.18.0.tgz
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Source Repository (Developer Access) git clone
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