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.
Competing Interest Statement
Tuuli Lappalainen is a paid adviser or consultant of Variant Bio, GSK, Pfizer and Goldfinch Bio. Francois Aguet is an employee and shareholder of Illumina Inc.
Footnotes
↵† Co-first author
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Posted July 20, 2022.
Multiset correlation and factor analysis enables exploration of multi-omic data
