CMiNet: R package for learning the Consensus Microbiome Network
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916479167889408 |
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| author | Aghdam, Rosa Solis-Lemus, Claudia |
| author_facet | Aghdam, Rosa Solis-Lemus, Claudia |
| contents | Understanding complex interactions within microbiomes is essential for exploring their roles in health and disease. However, constructing reliable microbiome networks often poses a challenge due to variations in the output of different network inference algorithms. To address this issue, we present CMiNet, an R package designed to generate a consensus microbiome network by integrating results from multiple established network construction methods. CMiNet incorporates nine widely used algorithms, including Pearson, Spearman, Biweight Midcorrelation (Bicor), SparCC, SpiecEasi, SPRING, GCoDA, and CCLasso, along with a novel algorithm based on conditional mutual information (CMIMN). By combining the strengths of these algorithms, CMiNet generates a single, weighted consensus network that provides a more stable and comprehensive representation of microbial interactions. The package includes customizable functions for network construction, visualization, and analysis, allowing users to explore network structures at different threshold levels and assess connectivity and reliability. CMiNet is designed to handle both quantitative and compositional data, ensuring broad applicability for researchers aiming to understand the intricate relationships within microbiome communities. Availability: Source code is freely available at https://github.com/solislemuslab/CMiNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08309 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CMiNet: R package for learning the Consensus Microbiome Network Aghdam, Rosa Solis-Lemus, Claudia Applications Computation Understanding complex interactions within microbiomes is essential for exploring their roles in health and disease. However, constructing reliable microbiome networks often poses a challenge due to variations in the output of different network inference algorithms. To address this issue, we present CMiNet, an R package designed to generate a consensus microbiome network by integrating results from multiple established network construction methods. CMiNet incorporates nine widely used algorithms, including Pearson, Spearman, Biweight Midcorrelation (Bicor), SparCC, SpiecEasi, SPRING, GCoDA, and CCLasso, along with a novel algorithm based on conditional mutual information (CMIMN). By combining the strengths of these algorithms, CMiNet generates a single, weighted consensus network that provides a more stable and comprehensive representation of microbial interactions. The package includes customizable functions for network construction, visualization, and analysis, allowing users to explore network structures at different threshold levels and assess connectivity and reliability. CMiNet is designed to handle both quantitative and compositional data, ensuring broad applicability for researchers aiming to understand the intricate relationships within microbiome communities. Availability: Source code is freely available at https://github.com/solislemuslab/CMiNet. |
| title | CMiNet: R package for learning the Consensus Microbiome Network |
| topic | Applications Computation |
| url | https://arxiv.org/abs/2411.08309 |