{"id":28956581,"url":"https://github.com/branchlab/metasnf","last_synced_at":"2025-06-23T21:32:58.579Z","repository":{"id":168327161,"uuid":"495904453","full_name":"BRANCHlab/metasnf","owner":"BRANCHlab","description":"Scalable subtyping with similarity network fusion","archived":false,"fork":false,"pushed_at":"2025-04-28T21:11:18.000Z","size":69938,"stargazers_count":8,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-06-20T19:07:05.819Z","etag":null,"topics":["bioinformatics","clustering","metaclustering","r","snf"],"latest_commit_sha":null,"homepage":"https://branchlab.github.io/metasnf/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BRANCHlab.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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}},"created_at":"2022-05-24T16:30:40.000Z","updated_at":"2025-04-28T20:56:06.000Z","dependencies_parsed_at":null,"dependency_job_id":"3f41deb4-b85e-468d-901c-7b8153e412a9","html_url":"https://github.com/BRANCHlab/metasnf","commit_stats":null,"previous_names":["branchlab/metasnf"],"tags_count":21,"template":false,"template_full_name":null,"purl":"pkg:github/BRANCHlab/metasnf","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BRANCHlab%2Fmetasnf","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BRANCHlab%2Fmetasnf/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BRANCHlab%2Fmetasnf/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BRANCHlab%2Fmetasnf/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BRANCHlab","download_url":"https://codeload.github.com/BRANCHlab/metasnf/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BRANCHlab%2Fmetasnf/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261558880,"owners_count":23177102,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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","clustering","metaclustering","r","snf"],"created_at":"2025-06-23T21:31:51.330Z","updated_at":"2025-06-23T21:32:58.572Z","avatar_url":"https://github.com/BRANCHlab.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\nbibliography: bibliography.bib\nlink-citations: yes\nlinkcolor: blue\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n    collapse = TRUE,\n    comment = \"#\u003e\",\n    fig.path = \"man/figures/README-\",\n    out.width = \"100%\"\n)\n```\n\n# metaSNF: Meta clustering with Similarity Network Fusion\n\n\u003c!-- badges: start --\u003e\n\u003c!-- badges: end --\u003e\n\n## Brief Overview\n\nmetaSNF is an R package that facilitates usage of the meta clustering paradigm described in [@caruanaMeta2006] with the similarity network fusion (SNF) data integration procedure developed in [@wangSimilarity2014].\nThe package offers a comprehensive suite of tools to assist users in transforming multi-modal tabular data into cluster solutions, decision making in the clustering process, and visualization along the way with a strong emphasis on context-specific utility and principled validation of results.\n\n## Installation\n\nYou will need R version 4.1.0 or higher to install this package.\nmetaSNF can be installed from CRAN:\n\n```{r eval = FALSE}\ninstall.packages(\"metasnf\")\n```\n\nDevelopment versions can be installed from GitHub:\n\n```{r eval = FALSE}\n# Latest development version\ndevtools::install_github(\"BRANCHlab/metasnf\")\n\n# Install a specific tagged version\ndevtools::install_github(\"BRANCHlab/metasnf@v2.1.2\")\n```\n\n## Quick Start\n\nMinimal usage of the package looks like this:\n\n```{r}\n# Load the package\nlibrary(metasnf)\n\n# Setting up the data\ndl \u003c- data_list(\n    list(abcd_cort_t, \"cortical_thickness\", \"neuroimaging\", \"continuous\"),\n    list(abcd_cort_sa, \"cortical_surface_area\", \"neuroimaging\", \"continuous\"),\n    list(abcd_subc_v, \"subcortical_volume\", \"neuroimaging\", \"continuous\"),\n    list(abcd_income, \"household_income\", \"demographics\", \"continuous\"),\n    list(abcd_pubertal, \"pubertal_status\", \"demographics\", \"continuous\"),\n    uid = \"patient\"\n)\n\n# Specifying 5 different sets of settings for SNF\nset.seed(42)\nsc \u003c- snf_config(\n    dl,\n    n_solutions = 5,\n    max_k = 40\n)\n\n# This matrix has clustering solutions for each of the 5 SNF runs!\nsol_df \u003c- batch_snf(dl, sc)\n\nsol_df\n\nt(sol_df)\n```\n\nCheck out the tutorial vignettes below to learn about how the package can be used:\n\n* [Simple usage of the package](https://branchlab.github.io/metasnf/articles/a_simple_example.html)\n* [Complex usage of the package](https://branchlab.github.io/metasnf/articles/a_complete_example.html)\n\nAnd more tutorials can be found under the \"articles\" section of the documentation home page: [https://branchlab.github.io/metasnf/index.html](https://branchlab.github.io/metasnf/index.html)\n\n\n## Background\n\n**Why use meta clustering?**\n\nClustering algorithms seek solutions where members of the same cluster are very similar to each other and members of distinct clusters are very dissimilar to each other.\nIn sufficiently noisy datasets where many qualitatively distinct solutions with similar scores of clustering quality exist, it is not necessarily the case that the top solution selected by a clustering algorithm will also be the most useful one for the user's context.\n\nTo address this issue, the original meta clustering procedure [Caruana et al., 2006](https://doi.org/10.1109/ICDM.2006.103) involved generating a large number of reasonable clustering solutions, clustering those solutions into qualitatively similar ones, and having the user examine those \"meta clusters\" to find something that seems like it'll be the most useful.\n\n**Why use SNF?**\n\nIn the clinical data setting, we often have access to patient data across a wide range of domains, such as imaging, genetics, biomarkers, demographics.\nWhen trying to extract subtypes out of all this information, direct concatenation of the data followed by cluster analysis can result in a substantial amount of lost (valuable) signal contained in each individual domain.\nEmpirically, SNF has been demonstrated to effectively integrate highly diverse patient data for the purposes of clinical subtyping.\n\n## Documentation\n\n### Example workflows\n\n* [Simple](https://branchlab.github.io/metasnf/articles/a_simple_example.html)\n* [Complex](https://branchlab.github.io/metasnf/articles/a_complete_example.html)\n\n### Essential objects\n\n* [SNF config](https://branchlab.github.io/metasnf/articles/snf_config.html)\n* [Data list](https://branchlab.github.io/metasnf/articles/data_list.html)\n\n### Further customization of generated solutions\n\n* [SNF schemes](https://branchlab.github.io/metasnf/articles/snf_schemes.html)\n* [Distance metrics](https://branchlab.github.io/metasnf/articles/distance_metrics.html)\n* [Clustering algorithms](https://branchlab.github.io/metasnf/articles/clustering_algorithms.html)\n* [Feature weighting](https://branchlab.github.io/metasnf/articles/feature_weights.html)\n\n### Additional functionality\n\n* [Stability measures and consensus clustering](https://branchlab.github.io/metasnf/articles/stability_measures.html)\n* [Removing unwanted signal](https://branchlab.github.io/metasnf/articles/confounders.html)\n* [Parallel processing](https://branchlab.github.io/metasnf/articles/parallel_processing.html)\n* [Label propagation](https://branchlab.github.io/metasnf/articles/label_propagation.html)\n* [Imputations](https://branchlab.github.io/metasnf/articles/imputations.html)\n* [NMI scores](https://branchlab.github.io/metasnf/articles/nmi_scores.html)\n\n### Plotting\n\n* [Correlation plots](https://branchlab.github.io/metasnf/articles/correlation_plots.html)\n* [Similarity matrix heatmaps](https://branchlab.github.io/metasnf/articles/similarity_matrix_heatmap.html)\n* [Manhattan plots](https://branchlab.github.io/metasnf/articles/manhattan_plots.html)\n* [Alluvial plots](https://branchlab.github.io/metasnf/articles/alluvial_plots.html)\n* [Feature plots](https://branchlab.github.io/metasnf/articles/feature_plots.html)\n\n## References\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbranchlab%2Fmetasnf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbranchlab%2Fmetasnf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbranchlab%2Fmetasnf/lists"}