{"id":30181241,"url":"https://github.com/multiomics-analytics-group/airbiome","last_synced_at":"2026-05-07T10:36:22.460Z","repository":{"id":309271698,"uuid":"963946991","full_name":"Multiomics-Analytics-Group/airbiome","owner":"Multiomics-Analytics-Group","description":"R and Python scripts to perform microbial network analysis of the AirBiome project,  from data preprocessing to network comparisons and metadata correlations.","archived":false,"fork":false,"pushed_at":"2025-08-18T18:27:06.000Z","size":9958,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-08-18T20:35:05.882Z","etag":null,"topics":["bioinformatics","metagenomics-analysis","network-analysis","python","r"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Multiomics-Analytics-Group.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-10T13:05:38.000Z","updated_at":"2025-08-18T18:27:09.000Z","dependencies_parsed_at":"2025-08-11T00:26:47.511Z","dependency_job_id":null,"html_url":"https://github.com/Multiomics-Analytics-Group/airbiome","commit_stats":null,"previous_names":["multiomics-analytics-group/airbiome"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Multiomics-Analytics-Group/airbiome","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Multiomics-Analytics-Group%2Fairbiome","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Multiomics-Analytics-Group%2Fairbiome/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Multiomics-Analytics-Group%2Fairbiome/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Multiomics-Analytics-Group%2Fairbiome/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Multiomics-Analytics-Group","download_url":"https://codeload.github.com/Multiomics-Analytics-Group/airbiome/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Multiomics-Analytics-Group%2Fairbiome/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32733937,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-07T02:14:30.463Z","status":"ssl_error","status_checked_at":"2026-05-07T02:14:29.405Z","response_time":62,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","metagenomics-analysis","network-analysis","python","r"],"created_at":"2025-08-12T08:07:42.652Z","updated_at":"2026-05-07T10:36:22.450Z","avatar_url":"https://github.com/Multiomics-Analytics-Group.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AirBiome Project: Microbial Network Analysis Workflow 🦠📊\n\nThis repository contains R and Python scripts organized into modules to perform microbial association network (MAN) analysis, \nfrom raw data preprocessing to network comparisons and metadata correlations. Here, we preprocess abundance and metadata files, build MANs\nusing the [NetComi R package](https://github.com/stefpeschel/NetCoMi/), filter and analyze these networks, visualize them in Cytoscape, \nand correlate network modules with metadata variables.\n\n---\n\n## Workflow Overview 🚀\n\nThe general workflow for analyzing MANs in this repository follows these steps:\n\n1.  **Preprocessing**: Prepare raw abundance and metadata for analysis. \n\n2.  **Network Inference**: Construct microbial association networks.\n\n3.  **Network Filtering**: Split networks based on various thresholds and remove singletons or low-connectivity nodes.\n\n4.  **Threshold Analysis**: Evaluate network topological metrics across networks with thresholds and identify optimal networks \nbased on modularity and average clsutering coefficient.\n\n5.  **Network Visualization**: Generate interactive visualizations of networks in Cytoscape.\n\n6.  **Network Analysis**: Perform network analysis using NetComi and custom Python scripts.\n\n7.  **Metadata Correlation**: Investigate relationships between network modules and metadata variables.\n\n8.  **Network Comparisons**: Compare the structure and composition of two distinct networks, overlaps, and clustering similarities.\n\n## Scripts \u0026 Their Purpose 📁\n\nEach subfolder within the `Scripts` directory contains specialized tools. For detailed usage instructions and parameters for individual scripts, refer to the `README.md` file located within each respective subfolder.\n\n| Folder | Purpose | Outputs |\n| :----------------------- | :------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| `Preprocessing` | Initial data handling: parsing raw data into usable formats and exploratory data analysis (EDA). | Cleaned abundance, taxonomic, and metadata files, EDA plots. |\n| `Network_inference` | Building microbial interaction networks with the **cclasso** method on **NetComi**. | `microNet` R objects and corresponding edgelist CSVs, often saved by network type and threshold. |\n| `Threshold_analysis` | Calculating and visualizing topological metrics of networks across different thresholds. | CSV files with calculated network metrics, PNG/SVG plots illustrating metric trends, and node clustering data for \"best\" networks. |\n| `Network_filtering` | Refining and cleaning inferred networks by applying thresholds and removing low-connectivity nodes. | Filtered `microNet` objects and cleaned edgelist CSVs (typically with `nosinglt` in the filename). |\n| `Network_visualization` | Creating dynamic visualizations of networks in Cytoscape. | Networks imported and visually styled within a running Cytoscape instance, mapping node properties (e.g., degree, cluster) and edge properties (e.g., weight, sign). |\n| `Network_analysis` | Advanced network analyses. | Various analytical results, potentially including centrality measures, multi-layer network structures, and other network-specific metrics. |\n| `Metadata_correlation` | Correlating microbial community modules with sample metadata. | CSV reports of module-metadata correlations (including statistical tests and FDR correction), and corresponding plots. |\n| `Network_comparisons` | Comparing different networks. | Metrics and reports on network overlap, unique features, and clustering similarities. |\n\nFor detailed instructions on running each script and understanding specific outputs, please refer to the individual `README.md` files located within each script subfolder (e.g., `Scripts/Network_filtering/README.md`).\n\n## Output Folders 📦\n\nThe `Output` directory is structured to store results from various analysis stages:\n\n* **`Best_nets`**: Stores information about optimal networks identified during threshold analysis. \nThe **.rds** files were ommitted from this repo because of their large size, but they can be generated by running the scripts in the `Threshold_analysis` folder.\n\n* **`EDA`**: Contains results from initial exploratory data analysis.\n\n* **`Threshold_analysis`**: Stores calculated metrics and plots from threshold analysis.\n\n## Summary Reports\n\nWe created sumamry reports with the main results of this project using [VueGen](https://github.com/Multiomics-Analytics-Group/vuegen), a tool to automate the generation of scientific reports:  \n\n[![HTML5](https://img.shields.io/badge/html5-%23E34F26.svg?style=for-the-badge\u0026logo=html5\u0026logoColor=white)][html-report]\n[![Streamlit](https://img.shields.io/badge/Streamlit-%23FE4B4B.svg?style=for-the-badge\u0026logo=streamlit\u0026logoColor=white)][streamlit-report]   \n\nFurther details on the reports and the source code to generate them is available on this [GitHub repository][report-github-repo].\n\n[html-report]: https://multiomics-analytics-group.github.io/airbiome_microb_asso_net_report/\n[streamlit-report]: https://airbiome-microb-ass-net-summary.streamlit.app/\n[report-github-repo]: https://github.com/Multiomics-Analytics-Group/airbiome_microb_asso_net_report\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmultiomics-analytics-group%2Fairbiome","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmultiomics-analytics-group%2Fairbiome","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmultiomics-analytics-group%2Fairbiome/lists"}