MOTL: enhancing multi-omics matrix factorization with transfer learning

Hirst, D.; Terezol, M.; Cantini, L.; Villoutreix, P.; Vignes, M.; Baudot, A.

doi:10.1101/2024.03.22.586210

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bioRxiv · 10.1101/2024.03.22.586210

MOTL: enhancing multi-omics matrix factorization with transfer learning

Hirst, D.·Terezol, M.·Cantini, L.·Villoutreix, P.·Vignes, M.·Baudot, A.

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Abstract

Joint matrix factorization is popular for extracting lower dimensional representations of multi-omics data but loses effectiveness with limited samples. Addressing this limitation, we introduce MOTL (Multi-Omics Transfer Learning), a framework that enhances MOFA (Multi-Omics Factor Analysis) by inferring latent factors for small multi-omics target datasets with respect to those inferred from a large heterogeneous learning dataset. We evaluated MOTL by designing simulated and real data protocols and demonstrated that MOTL improves the factorization of limited-sample multi-omics datasets when compared to factorization without transfer learning. When applied to actual glioblastoma samples, MOTL enhanced delineation of cancer status and subtype.

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Hirst, D., Terezol, M., Cantini, L., Villoutreix, P., Vignes, M., Baudot, A.. 2024-03-25. MOTL: enhancing multi-omics matrix factorization with transfer learning. https://doi.org/10.1101/2024.03.22.586210

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