Joint deep learning for batch effect removal and classification toward MALDI MS based metabolomics.

Area of Science:

• Metabolomics
• Bioinformatics
• Machine Learning

Background:

• Metabolomics is crucial for identifying metabolic signatures and biomarkers in clinical research.
• Batch effects, caused by external factors, significantly challenge the reliability of metabolomics studies.
• Deep learning models struggle to generalize across different data batches due to significant mismatches.

Purpose of the Study:

• To develop an end-to-end deep learning framework for simultaneous batch effect removal and classification of metabolomics data.
• To address the limitations of current methods in handling batch variations in omics data.
• To improve the accuracy and reliability of diagnostic models in metabolomics.

Main Methods:

• An end-to-end deep learning framework was proposed for joint batch effect removal and classification.
• The framework was validated on a public CyTOF dataset using simulated experiments.
• t-SNE visualization was employed to assess batch effect removal in the latent space.

Main Results:

• The deep learning framework effectively removed batch effects in the latent space, as shown by t-SNE distribution.
• On a private MALDI MS dataset, the method achieved the highest diagnostic accuracy.
• The proposed method demonstrated an average increase of 5.1–7.9% in diagnostic accuracy compared to state-of-the-art techniques.

Conclusions:

• The developed deep learning method significantly outperforms conventional approaches in classification tasks.
• Effective removal of batch effects is key to the superior performance of the proposed framework.
• The study highlights the potential of deep learning for robust biomarker discovery in metabolomics.