Computational and Statistical Framework for Quantitative Proteomics Analysis

Mass spectrometry-based quantitative proteomics usually produces large datasets that require exhaustive analysis to extract underlying biological information. This chapter presents a step-by-step pipeline for the statistical and computational analysis of such data, oriented and generalizable to any mass spectrometry-derived proteomic dataset. These steps include: (i) data preprocessing and quality control, (ii) identification of differentially abundant proteins through statistical modeling, (iii) functional enrichment analyses, including Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) to integrate proteomic changes in the context of biological processes and pathways, and, finally, (iv) interactome (protein-protein interaction) construction and visualization to situate proteomic alterations within signaling networks. Throughout, reproducible off-the-shelf R code and practical guidance for each step are provided, facilitating an end-to-end analysis from raw proteomic data to the biological interpretation, illustrated with visualization examples and best-practice recommendations. The complete script and necessary files are freely available at https://github.com/UMBB-IIS-Princesa/Quantitative-Proteomics-Pipeline.