Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.
Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.
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DOI：
10.15252/msb.202110240
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发表时间：
2021-08
影响因子：
9.9
通讯作者：
Pedrioli PGA
中科院分区：
文献类型：
--
作者：
Čuklina J;Lee CH;Williams EG;Sajic T;Collins BC;Rodríguez Martínez M;Sharma VS;Wendt F;Goetze S;Keele GR;Wollscheid B;Aebersold R;Pedrioli PGA
基于质谱的蛋白质组学的进步使实验涵盖了数百个样本。虽然这些大型样本集提供了急需的统计功效，但处理它们会引入称为批次效应的技术可变性。在这里，我们提出了一个逐步的协议，用于蛋白质组数据的评估，标准化和批量校正。我们回顾了相关领域建立的方法，并描述了针对蛋白质组学挑战的解决方案，如离子强度漂移和定量特征矩阵中的缺失值。最后，我们编写了一套技术，使控制批量效果调整质量。我们提供了一个R包，“proBatch”，包含协议每个步骤所需的函数。我们证明了这种方法的实用性，每个蛋白质组数据集包含数百个样本，并由多个实验设计。总之，我们提供了指导方针和工具，使从大型蛋白质组研究中提取真实的生物信号更加强大和透明，最终促进临床蛋白质组学和系统生物学的可靠和可重复的研究。在基于质谱的蛋白质组学中，处理大样本集会引入称为批次效应的技术可变性。本教程提供了用于蛋白质组学数据的评估、标准化和批量校正的指南和工具。
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. 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.
影响因子：
3.7
作者：
Landfors M;Philip P;Rydén P;Stenberg P
通讯作者：
Stenberg P