Multiset correlation and factor analysis enables exploration of multi-omic data

Brown, B. C.; Wang, C. L.; Kasela, S.; Aguet, F.; Nachun, D. C.; Taylor, K. D.; Tracy, R. P.; Durda, P.; Lui, Y.; Johnson, W. C.; Van Den Berg, D.; Gupta, N.; Gabriel, S.; Smith, J. D.; Gerzten, R.; Clish, C.; Wong, Q.; Papanicolau, G.; Blackwell, T. W.; Rotter, J. I.; Rich, S. S.; Ardlie, K. G.; Knowles, D. A.; Lappalainen, T.

doi:10.1101/2022.07.18.500246

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bioRxiv · 10.1101/2022.07.18.500246

Multiset correlation and factor analysis enables exploration of multi-omic data

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Abstract

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce Multi-set Correlation and Factor Analysis, an unsupervised integration method that enables fast inference of shared and private factors in multi-modal data. Applied to 614 ancestry-diverse participant samples across five omics types, MCFA infers a shared space that captures clinically relevant molecular processes.

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Brown, B. C., Wang, C. L., Kasela, S., Aguet, F., Nachun, D. C., Taylor, K. D., Tracy, R. P., Durda, P., Lui, Y., Johnson, W. C., Van Den Berg, D., Gupta, N., Gabriel, S., Smith, J. D., Gerzten, R., Clish, C., Wong, Q., Papanicolau, G., Blackwell, T. W., Rotter, J. I., Rich, S. S., Ardlie, K. G., Knowles, D. A., Lappalainen, T.. 2022-07-20. Multiset correlation and factor analysis enables exploration of multi-omic data. https://doi.org/10.1101/2022.07.18.500246

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