Interpretable multi-omics integration across mixed-order tensors with MANTRA

ABSTRACT: The integration of multi-modal molecular data is crucial for understanding complex diseases, but existing methods struggle with modern experimental designs that generate datasets with mixed-order tensors - for example, a third-order drug-response tensor alongside a second-order transcriptomics matrix. Here, we present MANTRA (Multi-view ANalysis with Tensor and matRix Alignment), a probabilistic framework that integrates collections of tensors of different orders, combining the strengths of group factor analysis and tensor decomposition. MANTRA learns interpretable latent factors and naturally handles missing data through a Bayesian approach with structured sparsity priors. On a Chronic Lymphocytic Leukemia dataset, the joint analysis of a third-order drug-response tensor and a second-ord

SUBMITTER: Kevin de Azevedo 

PROVIDER: S-SCDT-10_1038-S44320-026-00223-8 | biostudies-other |

REPOSITORIES: biostudies-other