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
There are three additional folders that contain R markdown notebook that were used to generate the plots for the paper:
approach_intuitioncontains the code to give an overview of the ideas underlying
compare_performancecontains the code to run
proDAon the Cox spike-in dataset and the de Graaf data and make the validation and comparison plots
ubiquitinationcontains 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).