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

Brielin C. Brown; Collin Wang; Silva Kasela; François Aguet; Daniel C. Nachun; Kent D. Taylor; Russell P. Tracy; Peter Durda; Yongmei Liu; W. Craig Johnson; David Van Den Berg; Namrata Gupta; Stacy B. Gabriel; Joshua D. Smith; Robert Edgardo Gerszten; Clary B. Clish; Quenna Wong; George J. Papanicolau; Thomas W. Blackwell; Jerome I. Rotter; Stephen S. Rich; R. Graham Barr; Kristin Ardlie; David A. Knowles; Tuuli Lappalainen

doi:10.1016/j.xgen.2023.100359

GenomicsCell GenomicsJul 10, 2023

Peer-reviewed journal article

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

Brielin C. Brown iD, Collin Wang, Silva Kasela iD, François Aguet iD, Daniel C. Nachun iD, Kent D. Taylor iD, Russell P. Tracy iD, Peter Durda iD, Yongmei Liu iD, W. Craig Johnson iD, David Van Den Berg iD, Namrata Gupta iD, Stacy B. Gabriel, Joshua D. Smith iD, Robert Edgardo Gerszten iD, Clary B. Clish iD, Quenna Wong iD, George J. Papanicolau, Thomas W. Blackwell iD, Jerome I. Rotter iD, Stephen S. Rich iD, R. Graham Barr iD, Kristin Ardlie iD, David A. Knowles iD, Tuuli Lappalainen iD

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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