Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial. Issue 8 (25th August 2021)

• Record Type: Journal Article
• Authors: Čuklina, Jelena; Lee, Chloe H; Williams, Evan G; Sajic, Tatjana; Collins, Ben C; Rodríguez Martínez, María; Sharma, Varun S; Wendt, Fabian; Goetze, Sandra; Keele, Gregory R; Wollscheid, Bernd; Aebersold, Ruedi; Pedrioli, Patrick G A
• Is Part Of: Molecular systems biology. Volume 17:Issue 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-25
• Subjects: batch effects -- data analysis -- large‐scale proteomics -- normalization -- quantitative proteomics; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.202110240
• Languages: English
• ISSNs: 1744-4292

Abstract

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. Abstract : In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data.