Thinking Points for Effective Batch Correction on Biomedical Data

Overview

Journal Brief Bioinform

Publisher Oxford University Press

Specialty Biology

Date 2024 Oct 14

PMID 39397427

Authors

Harvard Wai Hann Hui

Weijia Kong

Wilson Wen Bin Goh

Affiliations

Soon will be listed here.

Abstract

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.

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