{"id":20934602,"url":"https://github.com/biocore/gneiss","last_synced_at":"2025-04-09T20:06:55.382Z","repository":{"id":42519119,"uuid":"62265215","full_name":"biocore/gneiss","owner":"biocore","description":"compositional data analysis toolbox","archived":false,"fork":false,"pushed_at":"2022-12-15T23:45:59.000Z","size":34407,"stargazers_count":59,"open_issues_count":83,"forks_count":27,"subscribers_count":17,"default_branch":"master","last_synced_at":"2025-04-09T20:06:48.144Z","etag":null,"topics":["balance","compositional-statistics","omics","tree"],"latest_commit_sha":null,"homepage":"https://biocore.github.io/gneiss/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/biocore.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"COPYING.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-06-29T23:48:50.000Z","updated_at":"2025-03-30T00:42:55.000Z","dependencies_parsed_at":"2023-01-29T05:45:41.632Z","dependency_job_id":null,"html_url":"https://github.com/biocore/gneiss","commit_stats":null,"previous_names":[],"tags_count":15,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/biocore%2Fgneiss","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/biocore%2Fgneiss/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/biocore%2Fgneiss/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/biocore%2Fgneiss/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/biocore","download_url":"https://codeload.github.com/biocore/gneiss/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248103872,"owners_count":21048245,"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":["balance","compositional-statistics","omics","tree"],"created_at":"2024-11-18T22:09:58.585Z","updated_at":"2025-04-09T20:06:55.348Z","avatar_url":"https://github.com/biocore.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# gneiss\n\n[![Build Status](https://travis-ci.org/biocore/gneiss.png?branch=master)](https://travis-ci.org/biocore/gneiss)\n[![Coverage Status](https://coveralls.io/repos/biocore/gneiss/badge.svg)](https://coveralls.io/r/biocore/gneiss)\n[![Gitter](https://badges.gitter.im/biocore/gneiss.svg)](https://gitter.im/biocore/gneiss?utm_source=badge\u0026utm_medium=badge\u0026utm_campaign=pr-badge)\n\nCanonically pronouced *nice*\n\n\ngneiss is a compositional data analysis and visualization toolbox designed for analyzing high dimensional proportions.  See [here](https://biocore.github.io/gneiss/) for API documentation.\n \nNote that gneiss is not compatible with python 2, and is compatible with Python 3.4 or later.\ngneiss is currently in alpha.  We are actively developing it, and __backward-incompatible interface changes may arise__.\n\n# Installation\n\nTo install this package, it is recommended to use conda.  First make sure that the appropriate channels are configured.\n\n```\nconda config --add channels https://conda.anaconda.org/bioconda\nconda config --add channels https://conda.anaconda.org/biocore\nconda config --add channels https://conda.anaconda.org/qiime2\nconda config --add channels https://conda.anaconda.org/qiime2/label/r2017.6\n```\n\nThen gneiss can be installed in a conda environment as follows\n```\nconda create -n gneiss_env gneiss\n```\nTo install the most up to date version of gneiss, run the following command\n\n```\npip install git+https://github.com/biocore/gneiss.git\n```\n\n# Tutorials\n\n* [What are balances](https://github.com/biocore/gneiss/blob/master/ipynb/balance_trees.ipynb)\n\n# Qiime2 tutorials\n\n* [Linear regression on balances in the 88 soils](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/qiime2/88soils-qiime2-tutorial.html)\n* [Linear mixed effects models on balances in a CF study](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/qiime2/cfstudy-qiime2-tutorial.html)\n* [Linear regression on balances in the Chronic Fatigue Syndrome](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/qiime2/cfs-qiime2-tutorial.html)\n\n# Python tutorials\n\n* [Linear regression on balances in the 88 soils](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/python/88soils-python-tutorial.html)\n* [Linear mixed effects models on balances in a CF study](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/python/cfstudy-python-tutorial.html)\n* [Linear regression on balances in the Chronic Fatigue Syndrome](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/python/cfs-python-tutorial.html)\n\n\nIf you use this software package in your own publications, please cite it at\n```\nMorton JT, Sanders J, Quinn RA, McDonald D, Gonzalez A, Vázquez-Baeza Y, \nNavas-Molina JA, Song SJ, Metcalf JL, Hyde ER, Lladser M, Dorrestein PC, \nKnight R. 2017. Balance trees reveal microbial niche differentiation. \nmSystems 2:e00162-16. https://doi.org/10.1128/mSystems.00162-16.\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiocore%2Fgneiss","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbiocore%2Fgneiss","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiocore%2Fgneiss/lists"}