https://github.com/const-ae/proda-paper

https://github.com/const-ae/proda-paper

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• Host: GitHub
• URL: https://github.com/const-ae/proda-paper
• Owner: const-ae
• Created: 2019-06-03T08:50:13.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-04-30T10:56:37.000Z (over 4 years ago)
• Last Synced: 2024-11-06T14:00:49.858Z (about 2 months ago)
• Language: HTML
• Size: 66.3 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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](http://www.biorxiv.org/content/10.1101/661496v1) (Jun 2019)

This repository contains the code to reproduce the figures for the paper describing the 

[proDA](https://github.com/const-ae/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](#myfootnote1)

* Data studying the interaction landscape of Ubiquitin signalling by Xiaofei Zhang et al.[2](#myfootnote2)

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](https://htmlpreview.github.io/?https://github.com/const-ae/proDA-Paper/blob/master/approach_intuition/mean_variance_relation.nb.html)

    - [Location of missing values](https://htmlpreview.github.io/?https://github.com/const-ae/proDA-Paper/blob/master/approach_intuition/missing_value_location.nb.html)

    - [Probabilistic dropout model](https://htmlpreview.github.io/?https://github.com/const-ae/proDA-Paper/blob/master/approach_intuition/probabilistic_dropout_model.nb.html)

* `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](http://htmlpreview.github.io/?https://github.com/const-ae/proDA-Paper/blob/master/compare_performance/null_comparison.nb.html)

    - de Graaf semi-synthetic dataset performance comparison [notebook](https://htmlpreview.github.io/?https://github.com/const-ae/proDA-Paper/blob/master/compare_performance/compare_performance.nb.html)

* `ubiquitination` contains the code that was used to analyze the Ubiquitination data

    - [Analysis notebook](https://htmlpreview.github.io/?https://github.com/const-ae/proDA-Paper/blob/master/ubiquitination/Ubiquitination_Analysis.nb.html)

## 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).