Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization.
Automated pipeline
Multi-omics integration
Network analysis
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
24 Aug 2024
24 Aug 2024
Historique:
received:
07
04
2024
accepted:
14
08
2024
medline:
24
8
2024
pubmed:
24
8
2024
entrez:
23
8
2024
Statut:
epublish
Résumé
Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. AVAILABILITY : This package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/ .
Identifiants
pubmed: 39179997
doi: 10.1186/s12859-024-05900-9
pii: 10.1186/s12859-024-05900-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
276Subventions
Organisme : NIH HHS
ID : HL152725
Pays : United States
Organisme : NIH HHS
ID : HL152725
Pays : United States
Organisme : NIH HHS
ID : HL152725
Pays : United States
Organisme : NIH HHS
ID : HL152725
Pays : United States
Organisme : NIH HHS
ID : HL152725
Pays : United States
Organisme : NIH HHS
ID : HL152725
Pays : United States
Organisme : NHLBI NIH HHS
ID : https://topmed.nhlbi.nih.gov/awards/15744
Pays : United States
Informations de copyright
© 2024. The Author(s).
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