Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases

Litinetskaya, A.; Shulman, M.; Hediyeh-zadeh, S.; Moinfar, A. A.; Curion, F.; Szalata, A.; Omidi, A.; Lotfollahi, M.; Theis, F. J.

doi:10.1101/2024.07.29.605625

bioRxiv · 10.1101/2024.07.29.605625

Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases

Litinetskaya, A.·Shulman, M.·Hediyeh-zadeh, S.·Moinfar, A. A.·Curion, F.·Szalata, A.·Omidi, A.·Lotfollahi, M.·Theis, F. J.

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Abstract

To deliver clinically relevant insights from large patient cohorts profiled with single-cell technologies, a key challenge is to relate sample-level and single-cell measurements. We present MultiMIL, a deep learning framework that applies attention-based multiple-instance learning for phenotype prediction and cell state identification. We applied MultiMIL to peripheral blood mononuclear cells from COVID-19 patients, the Human Lung Cell Atlas, and a spatial proteomics breast cancer dataset, demonstrating how our model can be utilized to find phenotype-associated cell states, learn phenotype-informed sample representations, and expand disease signatures.

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Litinetskaya, A., Shulman, M., Hediyeh-zadeh, S., Moinfar, A. A., Curion, F., Szalata, A., Omidi, A., Lotfollahi, M., Theis, F. J.. 2024-07-29. Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases. https://doi.org/10.1101/2024.07.29.605625

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