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Thinking points for effective batch correction on biomedical data
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DOI：10.1093/bib/bbae515.png)
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Abstract
En 中文
Batch effects introduce significant variability into high-dimensional data, complicating accurate analysis and leading to potentially misleading conclusions if not adequately addressed. Despite technological and algorithmic advancements in biomedical research, effectively managing batch effects remains a complex challenge requiring comprehensive considerations. This paper underscores the necessity of a flexible and holistic approach for selecting batch effect correction algorithms (BECAs), advocating for proper BECA evaluations and consideration of artificial intelligence-based strategies. We also discuss key challenges in batch effect correction, including the importance of uncovering hidden batch factors and understanding the impact of design imbalance, missing values, and aggressive correction. Our aim is to provide researchers with a robust framework for effective batch effects management and enhancing the reliability of high-dimensional data analyses. Author summary: Batch effects introduce heterogeneity into data and hinder accurate analysis. Despite advancements in biomedical technologies and analysis algorithms, addressing batch effects remains a significant challenge due to their complex nature, which requires diverse considerations. To facilitate better handling of batch effects, we offer a comprehensive set of thinking points designed to help researchers ensure more robust analysis.
Keywords:
analysis
batch effects
biomedical informatics
data science
statistics
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