# MCQR: Enhancing the processing and analysis of quantitative proteomics data by incorporating chromatography and mass spectrometry information

Thierry Balliau, Anne Aubert-Frambourg, Olivier Langella, Martin-Magniette
Marie-Laure, Michel Zivy, Melisande Blein-Nicolas

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## To cite this version:

Thierry Balliau, Anne Aubert-Frambourg, Olivier Langella, Martin-Magniette Marie-Laure, Michel Zivy, et al.. MCQR: Enhancing the processing and analysis of quantitative proteomics data by incorporating chromatography and mass spectrometry information. EUPA 2025 saint-Malo, Jun 2025, Saint Malo (FR), France. hal-05113649

HAL Id: hal-05113649

https://cnrs.hal.science/hal-05113649v1

Submitted on 16 Jun 2025

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## Context and objective

In the field of proteomics, generating biologically relevant results from mass spectrometry signals remains a challenging task. This is partly due to the fact that the computational strategies for converting MS signals into biologically interpretable data depends heavily on the MS acquisition method. Additionally, the processing and the analysis of these data vary depending on whether the proteomic experiment was performed with or without labeling, and with or without fractionation. Several R packages have been developed for processing and analyzing MS data, but they only incorporate identification and quantification data; none of them takes into account other invaluable information collected during MS runs. To address this limitation, we introduce MCQR, an alternative R package for the in-depth exploration, processing, and analysis of quantitative proteomics data.

## Software design and availability

+ Modular architecture to accommodate various types of proteomics experiments, including label-free, label-based, fractionated, or those enriched for specific post-translational modifications.

+ Seven categories of functions: setting, import, calculation, plotting, filtering, extraction, and export.

+ Functions with self-explanatory names for ease of use.

+ Available as an external tool in i2MassChroQ (Langella et al. 2024)[1] and can be used in RStudio.

+ Source code released under the GPL license and available for download at https://forgemia.inra.fr/pappso/mcqr

+ Available user manual, tutorial, and command lines for installation on Windows, Debian, and Ubuntu

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<figure><img src="image_4.png" /><figcaption>Figure 1: Classical workflow for shotgun analysis. Entry point for the package can be both XIC data in masschroq TSV format or DIA-NN data from report.tsv file at peptide level and spectral count data at protein Level. Statistical analysis can be performed at protein or peptide levels for spectral count data or XIC data.</figcaption></figure>

## Features

+ Compatible with DIA-NN (Demichev et al 2020)[2] report files.

+ Combination of SC-based and XIC-based quantification

+ One function = one operation. Functions can be used as simple building blocks to test parameters and methods, and to construct customized analysis scenarios (3 pre-uploaded template available).

+ RT-based quality check, filtering, and normalisation (Figure 2)

+ Multiple peptide-to-protein aggregation methods: sum, mean, topN and model-based to take in account peptide effect

+ Two-levels missing value imputation approaches to address random and censored missingness

+ Statistical tests available at the peptide or protein levels :

- One- or two-way ANOVA

- Post-hoc Tukey test

- Student's T test

- **Contrast analysis to facilitate the examination of interaction effects in multi-factorial experiments**

+ Many descriptive analyses and representations : PCA, heatmap, protein clustering, volcano plot, Venn diagram

<figure><img src="image_5.png" /><figcaption>Figure 2: Illustration of the effect of normalisation method on the principal effect shown in PCA. A: representing the median intensity of peptide along the retention time before normalisation. B and C: the median intensity after normalisation by median.RT method and the resultant PCA representation showing a good separation for two different species. D and E: the median intensity after normalisation by median method and the resultant PCA</figcaption></figure>

## Conclusions

MCQR stands out from other similar R packages by incorporating chromatography and mass spectrometry information, enabling more thorough exploration and processing of quantitative proteomics data, and by proposing contrast analysis for a deep examination of interaction effects. Its modular, flexible architecture and its user-friendly functions allow users to address all aspects of mass spectrometry-based proteomics data, beyond just quantification. It also permits to filter and apply normalisation procedure based on retention time information. MCQR Package has been published in [3]