An R-Based Workflow for Analysis of Data Independent Acquisition (DIA) Neuroproteomics Data

Proteomic studies are highly informative for identifying biological signatures. The data generated from such experiments are highly complex and require substantial computational expertise to analyze appropriately. Rigorous analysis is further confounded by the presence of missing values and multiple sources of technical variance. Here, we present a methodical walkthrough of a workflow for the analysis of Data Independent Acquisition (DIA) proteomics data. The workflow is derived from the MultiScholaR framework, which aims to make best-in-class tools and practices available to researchers of all skill levels. Users will analyze a publicly available neuroproteomics dataset, searching data using DIA-NN, filtering and normalizing resultant data, before imputing missing values and removing technical variance using relevant tools in the field. Users will be able to generate easy to understand results on the individual protein and functional annotation level, with results and plots automatically formatted to be publication-ready. Designed for researchers of all skill levels, MultiScholaR emphasizes modularity, transparency, and learning through tunable parameters, extensive documentation, and automated reporting. Upon completion, users should feel equipped to analyze their own datasets confidently, bridging the gap between data generation and biological interpretation.