proDA-Paper

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.

Data

There are two datasets that are used for demonstration:

• Data on the phosphorylation dynamics from a study by Erik de Graaf et al.¹
• Data studying the interaction landscape of Ubiquitin signalling by Xiaofei Zhang et al.²

Both can be found in the data/ folder.

Analysis

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
  • Mean-variance relation
  • Location of missing values
  • Probabilistic dropout model
• compare_performance contains the code to run DEP, QPROT, Perseus, DAPAR, EBRCT, Triqler and proDA on the de Graaf data and make the validation and comparison plots
  • Null comparison on the de Graaf data set notebook
  • de Graaf semi-synthetic dataset performance comparison notebook
• ubiquitination contains the code that was used to analyze the Ubiquitination data
  • Analysis notebook

Sources

1. 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).

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