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SmCCNet 2.0: A Comprehensive Tool for Multi-omics Network Inference with Shiny Visualization.

Weixuan Liu1, Thao Vu1, Iain Konigsberg2

  • 1Department of Biostatistics and Informatics, School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA.

Biorxiv : the Preprint Server for Biology
|December 4, 2023
PubMed
Summary
This summary is machine-generated.

Sparse multiple canonical correlation network analysis (SmCCNet 2.0) integrates omics data with phenotypes to build disease-specific networks. This user-friendly tool enhances multi-omics data integration for complex disease research.

Keywords:
automated pipelinemulti-omics integrationnetwork analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Network Analysis

Background:

  • Integrating multi-omics data with disease phenotypes is crucial for understanding complex diseases.
  • Existing methods may lack flexibility or user-friendliness in network reconstruction.

Approach:

  • Introducing Sparse Multiple Canonical Correlation Network Analysis 2.0 (SmCCNet 2.0).
  • SmCCNet 2.0 integrates single or multiple omics data types with quantitative or binary phenotypes.
  • Offers a streamlined setup process, configurable manually or automatically, for user flexibility.

Key Points:

  • Enables the reconstruction of multi-omics networks specific to a variable of interest, such as disease phenotype.
  • Facilitates the integration of diverse omics data (e.g., genomics, transcriptomics, proteomics).
  • Provides a user-friendly interface and flexible configuration options.

Conclusions:

  • SmCCNet 2.0 is an advanced machine learning technique for variable-specific multi-omics network analysis.
  • The package enhances the integration and network-based analysis of complex biological data.
  • A network visualization tool is available to aid in the interpretation of results.