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Are batch effects still relevant in the age of big data?
Wilson Wen Bin Goh1, Chern Han Yong2, Limsoon Wong3
1Lee Kong Chian School of Medicine, Nanyang Technological University, 636921, Singapore; School of Biological Science, Nanyang Technological University, 637551, Singapore.
Trends in Biotechnology
|March 14, 2022
Summary
Batch effects (BEs) are technical biases in high-throughput data. New technologies like single-cell RNA sequencing increase BE complexity, requiring advanced mitigation strategies for accurate analysis.
Area of Science:
- Biotechnology
- Bioinformatics
- Genomics
Background:
- Batch effects (BEs) are technical biases that can confound analyses of high-throughput biotechnological data.
- Effective mitigation of BEs is complex and highly context-dependent.
- High-resolution technologies, such as single-cell RNA sequencing, introduce new challenges for BE management.
Purpose of the Study:
- To differentiate BE modeling between traditional and novel high-resolution datasets.
- To explore new methods for measuring and mitigating BEs, including significance assessment.
- To address the increasing complexities in BE management due to big data and advanced computational approaches.
Main Methods:
- Review and comparison of BE modeling techniques for diverse biotechnological datasets.
- Discussion of emerging strategies for BE quantification and correction.
- Analysis of the impact of machine learning and artificial intelligence on BE management.
Main Results:
- BE modeling differs significantly between traditional and high-resolution biotechnological data.
- New approaches for measuring and mitigating BEs are being developed.
- BEs are becoming increasingly important and complex in the era of big data.
Conclusions:
- Batch effects remain a critical challenge in high-throughput data analysis.
- The complexity of BEs is escalating with advancements in technology and data scale.
- Proactive and sophisticated BE management is essential for reliable biotechnological research.
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