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Currently available as a preprint:

Constantin Ahlmann-Eltze and Simon Anders: proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry. biorXiv 661496 (Jun 2019)

This repository contains the code to reproduce the figures for the paper describing the proDA R package.


There are three datasets that are used for demonstration:

  • Spike-in dataset with a mix of human and E. coli proteins by Cox et al.1
  • Data on the phosphorylation dynamics from a study by Erik de Graaf et al.2
  • Data studying the interaction landscape of Ubiquitin signalling by Xiaofei Zhang et al.3

All three can be found in the data/ folder.


There are three additional folders that contain R markdown notebook that were used to generate the plots for the paper:

  • approach_intuition contains the code to give an overview of the ideas underlying proDA
  • compare_performance contains the code to run DEP, QPROT, Perseus, DAPAR, EBRCT, MSqRob, MS-Empire, Triqler and proDA on the Cox spike-in dataset and the de Graaf data and make the validation and comparison plots
    • Spike-in dataset performance comparison notebook
    • de Graaf semi-synthetic dataset performance comparison notebook
  • ubiquitination contains the code that was used to analyze the Ubiquitination data


1. Cox, J. et al. Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014).

2. de Graaf, E. L., Giansanti, P., Altelaar, A. F. M. & Heck, A. J. R. Single-step Enrichment by Ti4 + -IMAC and Label-free Quantitation Enables In-depth Monitoring of Phosphorylation Dynamics with High Reproducibility and Temporal Resolution . Mol. Cell. Proteomics 13, 2426–2434 (2014).

3. Zhang, X. et al. An Interaction Landscape of Ubiquitin Signaling. Mol. Cell 65, 941–955.e8 (2017).

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