University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > MOFA: a principled framework for the unsupervised integration of multi-omics data

MOFA: a principled framework for the unsupervised integration of multi-omics data

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The emergence of high-throughput technologies and the increasing availability of clinical data are radically changing the study of biology and its medical applications. In particular, the profiling of multiple molecular layers (omics) from the same patient, provides a unique opportunity to build statistical models to understand the molecular sources of patient heterogeneity. I will present MOFA , a matrix factorisation framework for the comprehensive integration of multi-omics data. MOFA builds upon a Group Factor Analysis framework combined with fast variational Bayes inference. The model pools information across all -omics to reconstruct a low-dimensional representation of the data, thereby enhancing data interpretation and facilitating the definition of predictive models for clinical outcomes. To demonstrate its practical utility, I will present an application of MOFA on a cohort of 200 patient samples of chronic lymphocytic leukaemia that were profiled using multiple molecular assays, including somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including mutations on the immunoglobulin heavy-chain variable region and trisomy of chromosome 12.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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