{ "info": { "author": "Michal Krassowski", "author_email": "krassowski.michal+pypi@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering :: Bio-Informatics", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Utilities" ], "description": "GSEA API for Pandas\n===================\n\nPandas API for Gene Set Enrichment Analysis in Python (GSEApy, cudaGSEA,\nGSEA)\n\n- This Python wrapper around various GSEA implementations aims to\n provide a unified programming interface, built using the pandas\n DataFrames and a hierarchy of Pythonic classes.\n- The file exports (providing input for GSEA) were written with\n performance in mind, using lower level numpy functions where\n necessary, thus are much faster than usual pandas-based exports.\n- This project aims to allow scientists in the Python community to\n easily compare different implementations of GSEA, and to integrate\n those in projects which require high performance GSEA interface.\n- The project is in work-in-progress state and scheduled to have a\n major refactor and a more complete documentation.\n\nExample usage\n~~~~~~~~~~~~~\n\n.. code:: python\n\n from pandas import read_csv\n from gsea_api.expression_set import ExpressionSet\n from gsea_api.gsea import GSEADesktop\n from gsea_api.molecular_signatures_db import GeneMatrixTransposed\n\n reactome_pathways = GeneMatrixTransposed.from_gmt('ReactomePathways.gmt')\n\n gsea = GSEADesktop()\n\n design = ['Disease', 'Disease', 'Disease', 'Control', 'Control', 'Control']\n matrix = read_csv('expression_data.csv')\n\n result = gsea.run(\n # note: contrast() is not necessary in this simple case\n ExpressionSet(matrix, design).contrast('Disease', 'Control'),\n reactome_pathways,\n metric='Signal2Noise',\n permutations=1000\n )\n\nInstallation\n~~~~~~~~~~~~\n\nTo install the API use:\n\n::\n\n pip3 install gsea_api\n\nInstalling GSEA from Broad Institute\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nLogin/register on `the official GSEA\nwebsite `__ and\ndownload the ``gsea_3.0.jar`` file (or a newer version).\n\nPlease place the downloaded file in the thirdparty directory.\n\nInstalling GSEApy\n^^^^^^^^^^^^^^^^^\n\nTo use gsea.py please install it with:\n\n::\n\n pip3 install gseapy\n\nand link its binary to the ``thirdparty`` directory\n\n::\n\n ln -s virtual_environment_path/bin/gseapy thirdparty/gseapy\n\nInstalling cudaGSEA\n^^^^^^^^^^^^^^^^^^^\n\nPlease clone this fork of cudaGSEA to thirdparty directory and compile\nthe binary version:\n\n::\n\n git clone https://github.com/krassowski/cudaGSEA\n\nor use `the original version `__,\nwhich does not implement FDR calculations.\n\nCitation\n~~~~~~~~\n\nPlease cite the authors of the wrapped tools that you use.\n\nReferences\n~~~~~~~~~~\n\nThe initial version of this code was written for my `Master thesis\nproject `__\nat Imperial College London.", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/krassowski/gsea-api", "keywords": "gsea,gene,set,enrichment,cuda,pandas,api,GSEApy,cudaGSEA", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "gsea-api", "package_url": "https://pypi.org/project/gsea-api/", "platform": "", "project_url": "https://pypi.org/project/gsea-api/", "project_urls": { "Homepage": "https://github.com/krassowski/gsea-api" }, "release_url": "https://pypi.org/project/gsea-api/0.1.1/", "requires_dist": null, "requires_python": "", "summary": "Pandas API for Gene Set Enrichment Analysis in Python (GSEApy, cudaGSEA, GSEA)", "version": "0.1.1" }, "last_serial": 5519532, "releases": { "0.1": [ { "comment_text": "", "digests": { "md5": "643b98fd0f357c69085c0939a239ef68", "sha256": "e4d2b95dc224c879e9cd3b8d39ab097e6181b6506bad4b65f2c1c08bb0be8221" }, "downloads": -1, "filename": "gsea_api-0.1.tar.gz", "has_sig": false, "md5_digest": "643b98fd0f357c69085c0939a239ef68", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 10957, "upload_time": "2019-07-11T18:46:25", "url": "https://files.pythonhosted.org/packages/63/5f/e41aa8eee976d6037390c4531b8bab89a8011c77b74868305017d944efe6/gsea_api-0.1.tar.gz" } ], "0.1.1": [ { "comment_text": "", "digests": { "md5": "1cb99dbe077df581ec8731b4724308ad", "sha256": "0a6b5c240c7dec4236edcd10da322be85ebd1b023c053345843f5433a4448e27" }, "downloads": -1, "filename": "gsea_api-0.1.1.tar.gz", "has_sig": false, "md5_digest": "1cb99dbe077df581ec8731b4724308ad", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 10992, "upload_time": "2019-07-11T18:47:40", "url": "https://files.pythonhosted.org/packages/c4/fe/a9236912d737b56d2fc36cc72f96f6cb3a2d380abe28c3e4b09ce10f2229/gsea_api-0.1.1.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "1cb99dbe077df581ec8731b4724308ad", "sha256": "0a6b5c240c7dec4236edcd10da322be85ebd1b023c053345843f5433a4448e27" }, "downloads": -1, "filename": "gsea_api-0.1.1.tar.gz", "has_sig": false, "md5_digest": "1cb99dbe077df581ec8731b4724308ad", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 10992, "upload_time": "2019-07-11T18:47:40", "url": "https://files.pythonhosted.org/packages/c4/fe/a9236912d737b56d2fc36cc72f96f6cb3a2d380abe28c3e4b09ce10f2229/gsea_api-0.1.1.tar.gz" } ] }