Multi-omics integration and batch correction using a modality-agnostic deep learning framework

Alvira Larizgoitia, J. I.; Partel, G.; Venturelli, L.; Zhang, W.; Spotbeen, X.; Vanuytven, S.; Kint, S.; Vandereyken, K.; Wouters, D.; Ismail, A.; Scarceriaux, R.; Idkowiak, J.; Sarretto, T.; Ellis, S. R.; Loda, M.; Socciarelli, F.; Gevaert, T.; Joniau, S.; Claesen, M.; Verbeeck, N.; Voet, T.; Swinnen, J.; Jacobs, J.; Sifrim, A.

doi:10.1101/2025.10.21.683449

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bioRxiv · 10.1101/2025.10.21.683449

Multi-omics integration and batch correction using a modality-agnostic deep learning framework

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Abstract

State-of-the-art biotechnologies allow the detection of different molecular species on the same biological sample, generating complex highly-dimensional multi-modal datasets. Gaining a holistic understanding of biological phenomena, such as oncogenesis or aging, requires integrating these diverse modalities into low-dimensional data representations while correcting for technical artifacts. Here we present MIMA, a modular, unsupervised AI framework for multi-omics data integration and batch correction. Applied to complex spatial and single-cell datasets, MIMA effectively removes batch effects, while preserving biologically relevant information, and learns representations predictive of expert pathologist annotations. Additionally, it enables cross-modal translation, uncovers molecular patterns not captured by manual annotations, and despite being modality-agnostic performs on par with specialized state-of-the-art tools. MIMAs flexibility and scalability make it a powerful tool for multimodal data analysis. MIMA provides a foundation for AI-based, multi-omics augmented digital pathology frameworks, offering new opportunities for improved patient stratification and precision medicine through the comprehensive integration of high-dimensional molecular data and histopathological imaging.

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Alvira Larizgoitia, J. I., Partel, G., Venturelli, L., Zhang, W., Spotbeen, X., Vanuytven, S., Kint, S., Vandereyken, K., Wouters, D., Ismail, A., Scarceriaux, R., Idkowiak, J., Sarretto, T., Ellis, S. R., Loda, M., Socciarelli, F., Gevaert, T., Joniau, S., Claesen, M., Verbeeck, N., Voet, T., Swinnen, J., Jacobs, J., Sifrim, A.. 2025-10-22. Multi-omics integration and batch correction using a modality-agnostic deep learning framework. https://doi.org/10.1101/2025.10.21.683449

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