Enhancing Proteomics Quality Control: Insights from the Visualization Tool QCeltis

Large-scale mass-spectrometry-based proteomics experiments are complex and prone to analytical variability, requiring rigorous quality checks across each step in the workflow: sample preparation, chromatography, mass spectrometry, and the bioinformatics stages. This includes quality control (QC) measures that address biological and technical variation. Most QC approaches involve detecting sample outliers and monitoring parameters related to sample preparation and mass spectrometer performance. Evaluating these parameters regularly is essential for reliable downstream analysis and proteomics research. Here, we introduce "QCeltis", a Python package designed to facilitate automated QC analysis across the proteomics workflow, aiding in the identification of technical biases and consistency verification. QCeltis is a versatile tool for detecting QC issues in large-scale data-independent acquisition proteomics experiments by not only identifying sample preparation and acquisition issues but also aiding in differentiating between QC issues vs batch effects. QCeltis is available for command-line use in Windows and Linux environments. We present three case studies showcasing QCeltis's capabilities across different data sets, including depleted plasma, whole blood vs plasma, and dried blood spot samples, emphasizing its potential impact on large-scale proteomics projects. This package can be used to enhance data reliability and enable nuanced downstream analysis and interpretation for proteomics studies.