Hirst DPTérézol MCantini LVilloutreix PVignes MBaudot ACosgrove A2025-08-182025-08-182025-12-01Hirst DP, Térézol M, Cantini L, Villoutreix P, Vignes M, Baudot A. (2025). MOTL: enhancing multi-omics matrix factorization with transfer learning. Genome Biology. 26. 1.1474-7596https://mro.massey.ac.nz/handle/10179/73382Joint 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 evaluate MOTL by designing simulated and real data protocols and demonstrate 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 enhances delineation of cancer status and subtype.(c) 2025 The Author/sCC BY-NC-ND 4.0https://creativecommons.org/licenses/by-nc-nd/4.0/Matrix factorizationDimensionality reductionMulti-omicsData integrationTransfer learningMOFAJournal article10.1186/s13059-025-03675-71474-760Xjournal-article224s13059-025-03675-7