BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks

Simon J. Pelletier, Mickaël Leclercq, Florence Roux-Dalvai, Matthijs B. Geus, Shannon Leslie, Weiwei Wang, TuKiet T. Lam, Angus C. Nairn, Steven E. Arnold, Becky C. Carlyle, Frédéric Precioso and Arnaud Droit ()
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Simon J. Pelletier: CHU de Québec - Université Laval Research Center
Mickaël Leclercq: CHU de Québec - Université Laval Research Center
Florence Roux-Dalvai: CHU de Québec - Université Laval Research Center
Matthijs B. Geus: Massachusetts General Hospital Department of Neurology
Shannon Leslie: Yale Department of Psychiatry
Weiwei Wang: Yale School of Medicine
TuKiet T. Lam: Yale School of Medicine
Angus C. Nairn: Yale Department of Psychiatry
Steven E. Arnold: Massachusetts General Hospital Department of Neurology
Becky C. Carlyle: Massachusetts General Hospital Department of Neurology
Frédéric Precioso: Sophia Antipolis
Arnaud Droit: CHU de Québec - Université Laval Research Center

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions, and data acquisition techniques, significantly impacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of omics research, but current methods are not optimal for the removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. A comparison of batch effect correction methods across five diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that the overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.

Date: 2024
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DOI: 10.1038/s41467-024-48177-5

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