{"id":21280605,"url":"https://github.com/borgwardtlab/scones","last_synced_at":"2025-03-15T14:11:37.825Z","repository":{"id":69455320,"uuid":"71255603","full_name":"BorgwardtLab/scones","owner":"BorgwardtLab","description":"Selecting CONected Explanatory SNPs","archived":false,"fork":false,"pushed_at":"2016-10-18T14:48:16.000Z","size":334,"stargazers_count":0,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-22T04:14:00.974Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"C++","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/BorgwardtLab.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}},"created_at":"2016-10-18T14:15:16.000Z","updated_at":"2017-12-11T14:26:32.000Z","dependencies_parsed_at":"2023-10-20T18:34:30.632Z","dependency_job_id":null,"html_url":"https://github.com/BorgwardtLab/scones","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2Fscones","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2Fscones/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2Fscones/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2Fscones/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BorgwardtLab","download_url":"https://codeload.github.com/BorgwardtLab/scones/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243738987,"owners_count":20340002,"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":[],"created_at":"2024-11-21T10:37:19.553Z","updated_at":"2025-03-15T14:11:37.806Z","avatar_url":"https://github.com/BorgwardtLab.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SConES\n\n\n(**S**electing **Con**nected **E**xplanatory **S**NPs)\n\n## Summary\n\n\nMATLAB code implementing the method described in:  \n\u003e C.-A. Azencott, D. Grimm, M. Sugiyama, Y. Kawahara and K. Borgwardt (2013) \n\u003e **Efficient network-guided multi-locus association mapping with graph cuts**,  _Bioinformatics_ 29 (13), i171-i179  [doi:10.1093/bioinformatics/btt238](http://bioinformatics.oxfordjournals.org/content/29/13/i171)\n\n### Related work and repositories\n\n* **EasyGWAS**: SConES has been integrated to [EasyGWAS](https://github.com/dominikgrimm/easyGWASCore), a framework for the analysis and meta-analysis of GWAS data. In particular, this offers a Python interface. \n\n* **sfan**: Regarding the feature selection part (i.e. after the GWAS data has been processed and the SNP scored), [sfan](https://github.com/chagaz/sfan) uses a different (faster) maxflow solver, is written in Python, and also incorporates the multi-task version proposed in \n\u003e M. Sugiyama, C.-A. Azencott, D. Grimm, Y. Kawahara and K. Borgwardt (2014)\n\u003e **Multi-task feature selection on multiple networks via maximum flows**, _SIAM ICDM_, 199-207 [doi:10.1137/1.9781611973440.23](http://epubs.siam.org/doi/abs/10.1137/1.9781611973440.23)\n\n* **MultiSConeS**: For the original version of this multi-task version, see [Multi-SConES](https://github.com/mahito-sugiyama/Multi-SConES).\n\n\n## Demo:\n\nIn the `code` folder, there is a MATLAB script `demo.m`.  \n\nTo run the demo start MATLAB and type in:   \n\n```\ndemo()\n```\n\n\n## Data:\n\n* `X` = Genotypematrix of size `n x s`, where `n` is the number of samples and `s` is the number of SNPs  \n* `Y` = Phenotypevector of size `n x 1`, where `n` is the number of samples  \n* `W` = sparse network with size `s x s`  \n\n\nDemo files are provided in the `data` folder.\n\n\n## Running SConES for given values of the lambda and eta parameters\n`[indicators, objectives] = scones(data, option)`\n\n### Input:\nTo run SConES two parameters are needed. The first one is a data cell array:\n\n* `data.X` is the genotype data \n* `data.Y` is the phenotype  \n* `data.W` is the sparse network  \n* `data.selected_PCs` is the number of principal components that should be used for population structure correction  \n* `data.lambda_values` is a vector of size `1 x k` with `k` values for `lambda`  \n* `data.eta_values` is a vector of size `1 x h` with `h` values for `eta`  \n\nThe second parameter is a options cell array (optional - default values are specified):\n\n* `options.automatic` : if this parameter is true `data.lambda_values` and `data.eta_values` are determined automatically (default: `true`)  \n* `options.number_parameters` : this parameters specifices the number of eta and lambda values in the case `options.automatic` is set to true (default: `10`)  \n* `options.stdout` : if this parameter is true output is printed into the terminal window (default: `true`)  \n\n### Output: \n\n* indicators = indicator matrix of size `n x k x h`, where `n` is the length of vector `c`, `k` the length of vector `lambda_values` and `h` the length of vector `eta_values`\n* objectives = matrix with all objective values with size `k x h` for the grid of `lambda x eta` values\n\n## Using an inner cross-validation to determine the best values of the lambda and eta parameters\n`[indicators, objectives] = scones_crossvalidation(data, option)`\n\n### Input:\nThe first parameter (`data`) is the same as described above.\n\nThe second parameter (`options`) can additionally take the following values:\n* `options.nfold` : if `scones_crossvalidation` is called this parameter specifices the number of folds (default: `10`)  \n* `options.seed` : if `scones_crossvalidation` is called this parameter specifies a seed for splitting the data (default: 0)  \n\n## Contact \n\nAny questions can be directed to Chloe-Agathe Azencott: chloe-agathe.azencott [at] mines-paristech.fr\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fborgwardtlab%2Fscones","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fborgwardtlab%2Fscones","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fborgwardtlab%2Fscones/lists"}