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Smccnet 2.0：一个具有 shiny 可视化功能的用于多组学网络推断的综合工具。

Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization.

机构信息

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

Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.

出版信息

BMC Bioinformatics. 2024 Aug 24;25(1):276. doi: 10.1186/s12859-024-05900-9.

DOI:10.1186/s12859-024-05900-9
PMID:39179997
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11344457/
Abstract

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/ .

摘要

稀疏多元典型相关网络分析（SmCCNet）是一种机器学习技术，用于整合组学数据以及感兴趣的变量（例如复杂疾病的表型），并重建特定于该变量的多组学网络。我们提出了第二代 SmCCNet（SmCCNet 2.0），它能够灵活地整合单种或多种组学数据类型以及感兴趣的定量或二进制表型。此外，这个新软件包还提供了一个简化的设置过程，可手动或自动进行配置，确保了灵活且用户友好的体验。

可用性：这个软件包在 CRAN（https://cran.r-project.org/web/packages/SmCCNet/index.html）和 Github（https://github.com/KechrisLab/SmCCNet）上都有提供，遵循 MIT 许可证。网络可视化工具可在 https://smccnet.shinyapps.io/smccnetnetwork/ 上获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/e95fcac062fe/12859_2024_5900_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/4888595753f0/12859_2024_5900_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/56632fd55f83/12859_2024_5900_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/e342e243409d/12859_2024_5900_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/896e5050820d/12859_2024_5900_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/96628b7f7448/12859_2024_5900_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/f24f4e5509dc/12859_2024_5900_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/2ef8e4ece381/12859_2024_5900_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/d7308a0c2886/12859_2024_5900_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/71cc2a64fbd4/12859_2024_5900_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/7c2b502798df/12859_2024_5900_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/2c76e61c2a8e/12859_2024_5900_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/e95fcac062fe/12859_2024_5900_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/4888595753f0/12859_2024_5900_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/56632fd55f83/12859_2024_5900_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/e342e243409d/12859_2024_5900_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/896e5050820d/12859_2024_5900_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/96628b7f7448/12859_2024_5900_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/f24f4e5509dc/12859_2024_5900_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/2ef8e4ece381/12859_2024_5900_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/d7308a0c2886/12859_2024_5900_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/71cc2a64fbd4/12859_2024_5900_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/7c2b502798df/12859_2024_5900_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/2c76e61c2a8e/12859_2024_5900_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f68/11344457/e95fcac062fe/12859_2024_5900_Fig12_HTML.jpg

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引用本文的文献

[1]
BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool.

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本文引用的文献

[1]
Integrating airway microbiome and blood proteomics data to identify multi-omic networks associated with response to pulmonary infection.

Microbe. 2023-12

[2]
Multi-omics regulatory network inference in the presence of missing data.

Brief Bioinform. 2023-9-20

[3]
Canonical correlation analysis for multi-omics: Application to cross-cohort analysis.

PLoS Genet. 2023-5

[4]
NetSHy: network summarization via a hybrid approach leveraging topological properties.

Bioinformatics. 2023-1-1

[5]
SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-Omics Integration.

J Comput Biol. 2022-8

[6]
Identifying miRNA-mRNA Networks Associated With COPD Phenotypes.

Front Genet. 2021-10-28

[7]
Novel immune-related genes in the tumor microenvironment with prognostic value in breast cancer.

BMC Cancer. 2021-2-6

[8]
Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study.

Bioinformatics. 2020-11-1

[9]
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.

Genome Biol. 2020-5-11

[10]
Identifying Protein-metabolite Networks Associated with COPD Phenotypes.

Metabolites. 2020-3-25

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