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

Jelena Čuklina, Chloe H Lee, Evan G Williams, Tatjana Sajic, Ben C Collins, María Rodríguez Martínez, Varun S Sharma, Fabian Wendt, Sandra Goetze, Gregory R Keele, Bernd Wollscheid, Ruedi Aebersold, Patrick G A Pedrioli

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Abstract

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.
Original languageEnglish
Article numbere10240
Number of pages17
JournalMolecular systems biology
Volume17
Issue number8
DOIs
Publication statusPublished - 25 Aug 2021

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