Systematic Evaluation of Normalization Approaches in Tandem Mass Tag and Label-Free Protein Quantification Data Using PRONE
This repository contains all scripts that were produced when analyzing and evaluating the performance of different normalization methods on spike-in and real-world proteomics data sets.
First of all, you need to download the raw data from here. Put this data directory in the main directory of the repository.
To assess the performance of the normalization methods on the spike-in data sets, you need to run the Spike-in_Pipeline.R script. This script produces different folders of .rds objects that are required for the final evaluation of all spike-in data sets.
After the execution of the Spike-in_Pipeline.R, you can run Spike-in_Evaluation.R to evaluate the performance of the normalization methods and produce different types of plots.
To reproduce the results comparing limma to ROTS, use the script DE_Methods_Evaluation.R.
The scripts for the real-world datasets are available in the real-world directory. The scripts are named according to the dataset they are analyzing. To generate the plots of the paper, use the scripts Batch_Effect_Correction_Paper.R and DE_Analysis_Paper.R.
If you want to perform similar analyses on your proteomics dataset, have a look at the PRONE package available on Biocondcutor and GitHub. A web interface is accessible at https://exbio.wzw.tum.de/prone.
Lis Arend, Klaudia Adamowicz, Johannes R Schmidt, Yuliya Burankova, Olga Zolotareva, Olga Tsoy, Josch K Pauling, Stefan Kalkhof, Jan Baumbach, Markus List, Tanja Laske, Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE, Briefings in Bioinformatics, Volume 26, Issue 3, May 2025, bbaf201, https://doi.org/10.1093/bib/bbaf201.