{
    "info": {
        "author": "Wataru Hirota",
        "author_email": "hirota@whiro.me",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
        ],
        "description": "=======================================\nTopic Models for Single Cell Clustering\n=======================================\n\n\n.. image:: https://img.shields.io/pypi/v/tmscc.svg\n        :target: https://pypi.python.org/pypi/tmscc\n\n.. image:: https://img.shields.io/travis/tarohi24/tmscc.svg\n        :target: https://travis-ci.org/tarohi24/tmscc\n\n.. image:: https://readthedocs.org/projects/tmscc/badge/?version=latest\n        :target: https://tmscc.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n\n\n\nA package for reducing dimension of gene expression profiles and doing clustering them.\n\nInstallation\n-------\n.. code-block:: console\n\n   $ pip install tmscc\n\nfor more information, see https://tmscc.readthedocs.io/en/latest/installation.html.\n\nExample\n-------\n.. code-block:: python\n\n   from tmscc import tm\n   import numpy as np\n   import pandas as pd\n   from sklearn.cluster import KMeans\n\n   profile = pd.DataFrame(\n       np.arange(200).reshape([5, 40])\n   )  # gene expression profile (genes*cells matrix)\n   profile.index = ['CHEK2', 'MSH2', 'PTEN', 'TSC1', 'HER2']\n\n   lda = tm.LDA(\n       n_topics=4,\n       profile=profile,\n       outdir='~/tmp',\n   )\n   # LDA's estimation (This takes some time.)\n   lda.estimate()\n   # lda's theta() can be used for clustering, such as k-means\n   kmeans = KMeans(n_clusters=2).fit_predict(lda.theta())\n\n\n* Free software: MIT license\n* Documentation: https://tmscc.readthedocs.io.\n\n\nFeatures\n--------\n\n* TODO\n\n\nRequirements\n-------\n\n* Python >= 3.5\n* Java >= 1.8\n\nCredits\n-------\n\n* This package owes what this is to `Mallet`_. Thank you for the wonderful toolkit!\n  \n.. _Mallet: http://mallet.cs.umass.edu/\n\n\n=======\nHistory\n=======\n\n0.2.0 (2018-03-16)\n------------------\n\n* Add LDA implementation.\n* Add some documents.\n\n0.1.0 (2018-03-14)\n------------------\n\n* First release on PyPI.\n",
        "description_content_type": "",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/tarohi24/tmscc",
        "keywords": "tmscc",
        "license": "MIT license",
        "maintainer": "",
        "maintainer_email": "",
        "name": "tmscc",
        "package_url": "https://pypi.org/project/tmscc/",
        "platform": "",
        "project_url": "https://pypi.org/project/tmscc/",
        "project_urls": {
            "Homepage": "https://github.com/tarohi24/tmscc"
        },
        "release_url": "https://pypi.org/project/tmscc/0.3.0/",
        "requires_dist": null,
        "requires_python": "",
        "summary": "A package for reducing dimension of gene expression profilesand doing clustering them.",
        "version": "0.3.0"
    },
    "last_serial": 3686577,
    "releases": {
        "0.1.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "ea536e487b9d46e89a5c97cf56d082ad",
                    "sha256": "5e403b5ff4ff9b982ea0c58f8fa224e8abf31d829b26ce773b87cd247d75c86e"
                },
                "downloads": -1,
                "filename": "tmscc-0.1.0-py3.6.egg",
                "has_sig": false,
                "md5_digest": "ea536e487b9d46e89a5c97cf56d082ad",
                "packagetype": "bdist_egg",
                "python_version": "3.6",
                "requires_python": null,
                "size": 7428,
                "upload_time": "2018-03-15T03:27:22",
                "url": "https://files.pythonhosted.org/packages/9c/b2/428e515d1f56e2b32ed18fc33091243072618e122274a1a8a589fdafcd30/tmscc-0.1.0-py3.6.egg"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "140da30e93de52da6a5abc0ee17761d6",
                    "sha256": "9ccd13a4fca6ade9b350ad0b100eebae36a9c9544805d45465758414b8e37b04"
                },
                "downloads": -1,
                "filename": "tmscc-0.1.0.tar.gz",
                "has_sig": false,
                "md5_digest": "140da30e93de52da6a5abc0ee17761d6",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 14668,
                "upload_time": "2018-03-16T02:03:35",
                "url": "https://files.pythonhosted.org/packages/e3/04/b035b42d623d9b70e453b4efba5133bf001d25a8adc29d79336663a7013d/tmscc-0.1.0.tar.gz"
            }
        ],
        "0.2.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "5252297b49560dc21e0a0638a7cf6240",
                    "sha256": "802995f297eeeb20ca572b0c0283a2790d01aad021ff61acc8f5793ff01717f9"
                },
                "downloads": -1,
                "filename": "tmscc-0.2.0.tar.gz",
                "has_sig": false,
                "md5_digest": "5252297b49560dc21e0a0638a7cf6240",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 14667,
                "upload_time": "2018-03-16T02:19:16",
                "url": "https://files.pythonhosted.org/packages/29/67/119b1d461197acf74405027e565ff2bd1ed4d204bc8700d902ec9aaa9600/tmscc-0.2.0.tar.gz"
            }
        ],
        "0.3.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "7fa8260efb0b8ec04a45a4c8f632a9dc",
                    "sha256": "b1bd7b0c5690183fc96537ef91f7120cfbc3cc639a5d7784ab16aae32aac9d1a"
                },
                "downloads": -1,
                "filename": "tmscc-0.3.0.tar.gz",
                "has_sig": false,
                "md5_digest": "7fa8260efb0b8ec04a45a4c8f632a9dc",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 571448,
                "upload_time": "2018-03-20T05:30:16",
                "url": "https://files.pythonhosted.org/packages/bd/42/007dd77901008235fb6b18c01ff76d44b8420ee0f36654cdd6efef38a0f2/tmscc-0.3.0.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "7fa8260efb0b8ec04a45a4c8f632a9dc",
                "sha256": "b1bd7b0c5690183fc96537ef91f7120cfbc3cc639a5d7784ab16aae32aac9d1a"
            },
            "downloads": -1,
            "filename": "tmscc-0.3.0.tar.gz",
            "has_sig": false,
            "md5_digest": "7fa8260efb0b8ec04a45a4c8f632a9dc",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 571448,
            "upload_time": "2018-03-20T05:30:16",
            "url": "https://files.pythonhosted.org/packages/bd/42/007dd77901008235fb6b18c01ff76d44b8420ee0f36654cdd6efef38a0f2/tmscc-0.3.0.tar.gz"
        }
    ]
}