Unsupervised discovery of phenotype-specific multi-omics networks.

Algorithms Breast Neoplasms Gene Regulatory Networks Humans Phenotype Signal Transduction

Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
01 11 2019
Historique:
received: 12 06 2018
revised: 01 02 2019
accepted: 05 04 2019
pubmed: 9 4 2019
medline: 1 7 2020
entrez: 9 4 2019
Statut: ppublish

Résumé

Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA-mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA-mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. Supplementary data are available at Bioinformatics online.

Identifiants

DOI: 10.1093/bioinformatics/btz226 PMID: 30957844 PMC: PMC6931269
pubmed: 30957844
pii: 5430928
doi: 10.1093/bioinformatics/btz226
pmc: PMC6931269
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4336-4343

Subventions

Organisme : NHLBI NIH HHS
ID : R01 HL137995
Pays : United States
Organisme : NLM NIH HHS
ID : T15 LM009451
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL130512
Pays : United States
Organisme : NHLBI NIH HHS
ID : R21 HL140376
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL089856
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL126596
Pays : United States
Organisme : NIDA NIH HHS
ID : P30 DA044223
Pays : United States
Organisme : NHLBI NIH HHS
ID : K08 HL136928
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL124233
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL125583
Pays : United States
Organisme : NIAAA NIH HHS
ID : R01 AA021131
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL089897
Pays : United States
Organisme : NIAAA NIH HHS
ID : R24 AA013162
Pays : United States

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Auteurs

W Jenny Shi (WJ)

Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Yonghua Zhuang (Y)

Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Pamela H Russell (PH)

Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Brian D Hobbs (BD)

Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA.

Margaret M Parker (MM)

Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.

Peter J Castaldi (PJ)

Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.

Pratyaydipta Rudra (P)

Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Department of Statistics, Oklahoma State University, Stillwater, OK.

Brian Vestal (B)

Center for Genes, Environment & Health, National Jewish Health, Denver, CO, USA.

Craig P Hersh (CP)

Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA.

Laura M Saba (LM)

Department of Pharmaceutical Sciences, University of Colorado, Aurora, CO, USA.

Katerina Kechris (K)

Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

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Classifications MeSH

Recherchemedicale.com › Sciences de l'information › Méthodologies informatiques › Algorithmes › Unsupervised discovery of phenotype-specific...
Recherchemedicale.com › Maladies de la peau et du tissu conjonctif › Maladies de la peau › Maladies du sein › Tumeurs du sein › Unsupervised discovery of phenotype-specific...
Recherchemedicale.com › Phénomènes génétiques › Structures génétiques › Séquences d'acides nucléiques régulatrices › Réseaux de régulation génique › Unsupervised discovery of phenotype-specific...
Recherchemedicale.com › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Unsupervised discovery of phenotype-specific...
Recherchemedicale.com › Phénomènes génétiques › Phénotype › Unsupervised discovery of phenotype-specific...
Recherchemedicale.com › Phénomènes physiologiques cellulaires › Transduction du signal › Unsupervised discovery of phenotype-specific...