Open AccessDiagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial
Author(s) -
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
Publication year - 2021
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.202110240
Subject(s) - proteomics , normalization (sociology) , protocol (science) , computer science , quantitative proteomics , computational biology , data mining , sample size determination , biology , statistics , mathematics , medicine , biochemistry , alternative medicine , pathology , sociology , gene , anthropology
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.