This page contains example reports generated by the einprot R package, which provides a set of easy-to-use, reproducible workflows for statistical analysis of quantitative proteomics data.

Ostapcuk et al (2018) - MaxQuant

This example data set comes from

Ostapcuk et al: Activity-dependent neuroprotective protein recruits HP1 and CHD4 to control lineage-specifying genes, Nature 557:739-743 (2018).

Both raw and processed data are available from PRIDE (experiment 1356).

Quantification was performed using MaxQuant v1.5.3.8. The data set is provided as an example data set in einprot.

Ostapcuk et al (2018) - FragPipe

This is the same example data set as above, from

Ostapcuk et al: Activity-dependent neuroprotective protein recruits HP1 and CHD4 to control lineage-specifying genes, Nature 557:739-743 (2018).

Both raw and processed data are available from PRIDE (experiment 1356).

Quantification was performed using FragPipe v19.1. The data set is provided as an example data set in einprot.

Nie et al (2021) / He et al (2022) - Proteome Discoverer TMT 16-plex

This example data set was originally generated in

Nie et al: Multi-organ proteomic landscape of COVID-19 autopsies, Cell 184(3):775-791.e14 (2021)

It was reanalyzed with Proteome Discoverer and FragPipe by

He et al: Comparative Evaluation of Proteome Discoverer and FragPipe for the TMT-based proteome quantification, Journal of Proteome Research 21(12):3007-3015 (2022).

Here, we downloaded the processed data from He et al (2022) from the supplementary material, and analyzed the Proteome Discoverer output from the 16-plex TMT experiment using einprot. The data set contains samples from renal cortex and renal medulla from patients with and without COVID-19. The samples were processed in four batches.

Shen et al (2018) / Wolski et al (2023) - IonStar human/E.coli spike-in (MaxQuant)

This data set was originally generated in

Shen et al: IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts. Proceedings of the National Academy of Sciences 115(21):E4767-E4776 (2018)

It was requantified with MaxQuant by

Wolski et al: prolfqua: A comprehensive R-package for proteomics differential expression analysis. Journal of Proteome Research 22(4):1092-1104 (2023)

We downloaded the processed MaxQuant data from Wolski et al (2023) from the associated GitLab repository and analyzed it with einprot. The data set consists of 20 samples of a constant human background with varying degrees of E.coli lysate spiked in, leading to different expected log-fold changes for the E.coli proteins between different groups of samples.