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Review of Batch Effects Prevention, Diagnostics, and Correction Approaches
Jelena Čuklina1,2, Patrick G A Pedrioli1,3, Ruedi Aebersold4,5
1Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
Methods in Molecular Biology (Clifton, N.J.)
|September 26, 2019
Summary
Batch effects are systematic variations in high-throughput studies that can obscure biological signals. This study offers recommendations for designing large-scale proteomic studies and correcting batch effects to improve data quality.
Area of Science:
- Proteomics
- Bioinformatics
- Genomics
Background:
- Batch effects are systematic technical variations in high-throughput studies that reduce biological signal sensitivity and introduce artifacts.
- These effects are common in large sample cohorts due to logistical constraints requiring samples to be processed in batches.
- While extensively studied in genomics, batch effects are a newer challenge in proteomics due to the recent availability of high-throughput measurement methods.
Purpose of the Study:
- To provide general recommendations for mitigating batch effects in large-scale proteomic studies.
- To discuss strategies for experimental design to minimize batch effects.
- To review common batch effect correction tools and their application in proteomics.
Main Methods:
- Review of existing literature on batch effects and correction methods in genomics and proteomics.
- Discussion of experimental design principles for large-scale proteomic studies.
- Overview of commonly used statistical tools for batch effect correction.
Main Results:
- Batch effects pose a significant challenge in large-scale proteomic studies, impacting data reliability.
- Careful experimental design is crucial for mitigating batch effects.
- Several statistical tools are available for batch effect correction in proteomics, with varying applications.
Conclusions:
- Mitigating batch effects is essential for accurate biological signal extraction in high-throughput proteomics.
- Recommendations for study design and the application of correction tools can improve data quality.
- Further research and standardization of methods are needed to address batch effects in proteomics.
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