Multiset correlation and factor analysis enables exploration of multi-omics 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 Gabriel; Joshua D Smith; Robert Gerzsten; Clary Clish; Quenna Wong; George Papanicolau; Thomas W Blackwell; Jerome I Rotter; Stephen S Rich; R Graham Barr; Kristin G Ardlie; David A Knowles; Tuuli Lappalainen

doi:10.1016/j.xgen.2023.100359

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

Cell Genom. 2023 Jul 10;3(8):100359. doi: 10.1016/j.xgen.2023.100359. eCollection 2023 Aug 9.

Authors

Brielin C Brown^{1

2}, Collin Wang^{1

3}, Silva Kasela^{1

4}, François Aguet^{5

6}, Daniel C Nachun⁷, Kent D Taylor⁸, Russell P Tracy⁹, Peter Durda⁹, Yongmei Liu¹⁰, W Craig Johnson¹¹, David Van Den Berg¹², Namrata Gupta⁶, Stacy Gabriel⁶, Joshua D Smith¹³, Robert Gerzsten¹⁴, Clary Clish⁶, Quenna Wong¹¹, George Papanicolau¹⁵, Thomas W Blackwell¹⁶, Jerome I Rotter⁸, Stephen S Rich¹⁷, R Graham Barr¹⁸, Kristin G Ardlie⁶, David A Knowles^{1

2

3

4}, Tuuli Lappalainen^{1

4

19}

Affiliations

¹ New York Genome Center, New York, NY, USA.
² Data Science Institute, Columbia University, New York, NY, USA.
³ Department of Computer Science, Columbia University, New York, NY, USA.
⁴ Department of Systems Biology, Columbia University, New York, NY, USA.
⁵ Illumina Incorporated, San Francisco, CA, USA.
⁶ The Broad Institute of MIT and Harvard, Boston, MA, USA.
⁷ Department of Pathology, Stanford University, Stanford, CA, USA.
⁸ Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
⁹ Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
¹⁰ Department of Medicine, Duke University Medical Center, Durham, NC, USA.
¹¹ Department of Biostatistics, University of Washington, Seattle, WA, USA.
¹² Department of Clinical Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
¹³ Northwest Genomics Center, University of Washington, Seattle, WA, USA.
¹⁴ Beth Israel Deaconess Medical Center, Division of Cardiovascular Medicine, Boston, MA, USA.
¹⁵ Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.
¹⁶ Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
¹⁷ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
¹⁸ Mailman School of Public Health, Columbia University, New York, NY, USA.
¹⁹ Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.

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 (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

Abstract

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