{"id":18598219,"url":"https://github.com/cmdoret/hicreppy","last_synced_at":"2025-04-10T18:30:49.940Z","repository":{"id":57437290,"uuid":"225617650","full_name":"cmdoret/hicreppy","owner":"cmdoret","description":"Python reimplementation of hicrep with compatibility for sparse matrices","archived":false,"fork":false,"pushed_at":"2022-12-18T21:30:40.000Z","size":224,"stargazers_count":17,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-25T01:51:08.623Z","etag":null,"topics":["bioinformatics","genomics","hi-c","reproducibility"],"latest_commit_sha":null,"homepage":null,"language":"Python","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/cmdoret.png","metadata":{"files":{"readme":"README.md","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}},"created_at":"2019-12-03T12:44:11.000Z","updated_at":"2024-08-05T20:07:46.000Z","dependencies_parsed_at":"2023-01-29T20:00:24.341Z","dependency_job_id":null,"html_url":"https://github.com/cmdoret/hicreppy","commit_stats":null,"previous_names":[],"tags_count":7,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmdoret%2Fhicreppy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmdoret%2Fhicreppy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmdoret%2Fhicreppy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmdoret%2Fhicreppy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cmdoret","download_url":"https://codeload.github.com/cmdoret/hicreppy/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248271568,"owners_count":21075800,"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","genomics","hi-c","reproducibility"],"created_at":"2024-11-07T01:31:41.332Z","updated_at":"2025-04-10T18:30:49.643Z","avatar_url":"https://github.com/cmdoret.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# hicreppy\n**cmdoret**\n\n\n[![PyPI version](https://badge.fury.io/py/hicreppy.svg)](https://badge.fury.io/py/hicreppy)\n[![build](https://github.com/cmdoret/hicreppy/actions/workflows/build.yml/badge.svg)](https://github.com/cmdoret/hicreppy/actions/workflows/build.yml)\n[![codecov](https://codecov.io/gh/cmdoret/hicreppy/branch/master/graph/badge.svg)](https://codecov.io/gh/cmdoret/hicreppy)\n[![Codacy Badge](https://app.codacy.com/project/badge/Grade/07e29576e19447a18783303d12ce2539)](https://www.codacy.com/gh/cmdoret/hicreppy/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=cmdoret/hicreppy\u0026amp;utm_campaign=Badge_Grade)\n\nThis is a python reimplementation of hicrep's algorithm with added support for sparse matrices (in .cool format). \n\nhicrep measures similarity between Hi-C samples by computing a stratum-adjusted correlation coefficient (SCC). In this implementation, the SCC is computed separately for each chromosome and the chromosome length-weighted average of SCCs is computed.\n\nhicrep is published at:\n\u003e HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Tao Yang, Feipeng Zhang, Galip Gurkan Yardimci, Ross C Hardison, William Stafford Noble, Feng Yue, Qunhua Li, 2017, Genome Research, doi: 10.1101/gr.220640.117\n\nThe original implementation, in R can be found at https://github.com/MonkeyLB/hicrep\n\n### Installation\n\nYou can install the package using pip:\n\n```bash\npip install --user hicreppy\n```\n\n### Usage\n\nTo find the optimal value for smoothing parameter h, you can use the htrain subcommand:\n\n```\n\nUsage: hicreppy htrain [OPTIONS] COOL1 COOL2\n\n  Find the optimal value for smoothing parameter h. The optimal h-value is\n  printed to stdout. Run informations are printed to stderr.\n\nOptions:\n  -r, --h-max INTEGER     Maximum value of the smoothing parameter h to\n                          explore. All consecutive integer values from 0 to\n                          this value will be tested.  [default: 10]\n  -m, --max-dist INTEGER  Maximum distance at which to compute the SCC, in\n                          basepairs.  [default: 100000]\n  -b, --blacklist TEXT    Exclude those chromosomes in the analysis. List of\n                          comma-separated chromosome names.\n  -w, --whitelist TEXT    Only include those chromosomes in the analysis. List\n                          of comma-separated chromosome names.\n  --help                  Show this message and exit.\n\n```\n\nTo compute the SCC between two matrices, use the scc subcommand. The optimal h value obtained with htrain should be provided to the flag `-v`:\n\n```\n\nUsage: hicreppy scc [OPTIONS] COOL1 COOL2\n\n  Compute the stratum-adjusted correlation coefficient for input matrices\n\nOptions:\n  -v, --h-value INTEGER    Value of the smoothing parameter h to use. Should\n                           be an integer value \u003e= 0.  [default: 10]\n  -m, --max-dist INTEGER   Maximum distance at which to compute the SCC, in\n                           basepairs.  [default: 100000]\n  -s, --subsample INTEGER  Subsample contacts from both matrices to target\n                           value. Leave to 0 to disable subsampling.\n                           [default: 0]\n  -b, --blacklist TEXT     Exclude those chromosomes in the analysis. List of\n                           comma-separated chromosome names.\n  -w, --whitelist TEXT     Only include those chromosomes in the analysis.\n                           List of comma-separated chromosome names.\n  --help                   Show this message and exit.\n```\n\nWhen running multiple pairwise comparisons, compute the optimal h value once between two highly similar samples and reuse the h value for all `scc` commands\n\n### Contributing\n\nAll contributions are welcome. We use the [numpy standard](https://numpydoc.readthedocs.io/en/latest/format.html) for docstrings when documenting functions.\n\nThe code formatting standard we use is [black](https://github.com/psf/black), with --line-length=79 to follow PEP8 recommendations. We use `pytest` with the `pytest-doctest` and `pytest-pylint` plugins as our testing framework. Ideally, new functions should have associated unit tests, placed in the `tests` folder.\n\nTo test the code, you can run:\n\n```bash\npytest --doctest-modules --pylint --pylint-error-types=EF --pylint-rcfile=.pylintrc hicreppy tests\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcmdoret%2Fhicreppy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcmdoret%2Fhicreppy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcmdoret%2Fhicreppy/lists"}