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

Loading next page...
 
/lp/wiley/integrative-analysis-of-transcriptomic-and-metabolomic-data-via-sparse-ywmPpYUigJ
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

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off