Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial

Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, proBatch, containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.

Automated summary

This study discusses proteomic experiments involving hundreds of samples, and how to assess, normalize, and correct batch effects in proteomic data. The research reviews methodologies from various fields and offers solutions specific to challenges in proteomics, ultimately providing a set of techniques for controlling the quality of batch effect adjustment.