Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information

Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation... IntroductionRecent advancement in high‐throughput, biomedical technologies has enabled the measurement of multiple high‐dimensional omics data types in a single study, including genomics, epigenomics, transcriptomics, and metabolomics. Each of these data types provides a different snapshot of the underlying biological system, and combining multiple data types has been shown to be very valuable in investigating complex diseases. It has been demonstrated that individual components in these data are functionally structured in networks or pathways and incorporation of such structural (or biological) information can improve analysis and lead to biologically more meaningful results (Li and Li, ; Pan et al., ; Chen et al., ). By the same token, it is desirable to jointly assess the association between these data types with incorporation of available structural information for each data type, enabling us to uncover drivers that individually or in combination provide better insight about the biological mechanism. In this article, we develop new canonical correlation analysis (CCA) methods for studying the overall dependency structure between transcripts and metabolites while incorporating structural information for each data type.The PHI StudyOur work is motivated by data from the Emory University and Georgia Tech Predictive Health Institute (PHI) study. The PHI was established in http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12715
Publisher site
See Article on Publisher Site

Abstract

IntroductionRecent advancement in high‐throughput, biomedical technologies has enabled the measurement of multiple high‐dimensional omics data types in a single study, including genomics, epigenomics, transcriptomics, and metabolomics. Each of these data types provides a different snapshot of the underlying biological system, and combining multiple data types has been shown to be very valuable in investigating complex diseases. It has been demonstrated that individual components in these data are functionally structured in networks or pathways and incorporation of such structural (or biological) information can improve analysis and lead to biologically more meaningful results (Li and Li, ; Pan et al., ; Chen et al., ). By the same token, it is desirable to jointly assess the association between these data types with incorporation of available structural information for each data type, enabling us to uncover drivers that individually or in combination provide better insight about the biological mechanism. In this article, we develop new canonical correlation analysis (CCA) methods for studying the overall dependency structure between transcripts and metabolites while incorporating structural information for each data type.The PHI StudyOur work is motivated by data from the Emory University and Georgia Tech Predictive Health Institute (PHI) study. The PHI was established in

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ; ; ;

References

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