Metadata-Version: 1.1
Name: gopca
Version: 0.2.1
Summary: GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge
Home-page: https://github.com/flo-compbio/gopca
Author: Florian Wagner
Author-email: florian.wagner@duke.edu
License: GPLv3
Description: ..
            Copyright (c) 2015, 2016 Florian Wagner
            
            This file is part of GO-PCA.
            
            GO-PCA is free software: you can redistribute it and/or modify
            it under the terms of the GNU General Public License, Version 3,
            as published by the Free Software Foundation.
            
            This program is distributed in the hope that it will be useful,
            but WITHOUT ANY WARRANTY; without even the implied warranty of
            MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
            GNU General Public License for more details.
            
            You should have received a copy of the GNU General Public License
            along with this program. If not, see <http://www.gnu.org/licenses/>.
        
        GO-PCA
        ======
        
        | |pypi| |versions| |license|
        
        ===========  =================================================
        **latest**   |travis-latest| |codecov-latest| |docs-latest|
        **develop**  |travis-develop| |codecov-develop| |docs-develop|
        ===========  =================================================
        
        GO-PCA (`Wagner, 2015`__) is an unsupervised method to **explore gene
        expression data using prior knowledge**. This is a free and open-source
        implementation of GO-PCA in Python.
        
        __ go_pca_paper_
        
        Briefly, GO-PCA combines `principal component analysis (PCA)`__  with
        `nonparametric GO enrichment analysis`__ in order to generate **signatures**,
        i.e., small sets of genes that are both strongly correlated and closely
        functionally related. It then visualizes the expression profiles of all
        signatures in a **signature matrix**, designed to serve as a systematic and
        easily interpretable representation of biologically relevant expression
        patterns.
        
        __ pca_
        __ go_enrich_
        
        .. _go_pca_paper: https://dx.doi.org/10.1371/journal.pone.0143196
        .. _pca: https://en.wikipedia.org/wiki/Principal_component_analysis
        .. _go_enrich: https://dx.doi.org/10.1186/1471-2105-10-48
        
        Links
        -----
        
        - `Demos <https://github.com/flo-compbio/gopca-demos>`_
        - `Documentation <https://gopca.readthedocs.org/en/latest>`_
        - `Download of GO-derived gene sets <https://www.dropbox.com/sh/m0r7uqnfdr5x0xu/AADqqJ-8VzPchBRhDm50QxWaa?dl=0>`_
        - `PLoS One paper <https://dx.doi.org/10.1371/journal.pone.0143196>`_
        
        Support and Development
        -----------------------
        
        - For feature requests and bug reports, please create an `issue`__ on GitHub.
        - For technical questions, please feel free to `email`__.
        - If you want to contribute code to GO-PCA, please `email`__ and/or create a
          pull request on GitHub.
        - For a list of the latest changes, please see the
          `Changelog <CHANGELOG.rst>`_.
        
        __ github_issue_
        __ email_
        __ email_
        
        .. _github_issue: https://github.com/flo-compbio/gopca/issues
        .. _email: mailto:florian.wagner@duke.edu
        
        How to Cite GO-PCA
        ------------------
        
        If you use GO-PCA in your research, please cite `Wagner (PLoS One, 2015)`__
        
        __ wagner_pone_
        
        .. _wagner_pone: https://dx.doi.org/10.1371/journal.pone.0143196
        
        Copyright and License
        ---------------------
        
        Copyright (c) 2015, 2016 Florian Wagner
        
        ::
        
          GO-PCA is free software: you can redistribute it and/or modify
          it under the terms of the GNU General Public License, Version 3,
          as published by the Free Software Foundation.
          
          This program is distributed in the hope that it will be useful,
          but WITHOUT ANY WARRANTY; without even the implied warranty of
          MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
          GNU General Public License for more details.
          
          You should have received a copy of the GNU General Public License
          along with this program. If not, see <http://www.gnu.org/licenses/>.
        
        .. |pypi| image:: https://img.shields.io/pypi/v/gopca.svg
            :target: https://pypi.python.org/pypi/gopca
            :alt: PyPI version
        
        .. |versions| image:: https://img.shields.io/pypi/pyversions/gopca.svg
            :target: https://pypi.python.org/pypi/gopca
            :alt: Python versions supported
        
        .. |license| image:: https://img.shields.io/pypi/l/gopca.svg
            :target: https://pypi.python.org/pypi/gopca
            :alt: License
        
        .. |travis-latest| image:: https://travis-ci.org/flo-compbio/gopca.svg?branch=master
            :alt: Build Status (master branch)
            :scale: 100%
            :target: https://travis-ci.org/flo-compbio/gopca
        
        .. |travis-develop| image:: https://travis-ci.org/flo-compbio/gopca.svg?branch=develop
            :alt: Build Status (develop branch)
            :scale: 100%
            :target: https://travis-ci.org/flo-compbio/gopca
        
        .. |codecov-latest| image:: https://codecov.io/github/flo-compbio/gopca/coverage.svg?branch=master
            :alt: Coverage (master branch)
            :target: https://codecov.io/github/flo-compbio/gopca?branch=master
        
        .. |codecov-develop| image:: https://codecov.io/github/flo-compbio/gopca/coverage.svg?branch=develop
            :alt: Coverage (develop branch)
            :target: https://codecov.io/github/flo-compbio/gopca?branch=develop
        
        .. |docs-latest| image:: https://readthedocs.org/projects/gopca/badge/?version=latest
            :alt: Documentation Status (master branch)
            :scale: 100%
            :target: https://gopca.readthedocs.org/en/latest
        
        .. |docs-develop| image:: https://readthedocs.org/projects/gopca/badge/?version=develop
            :alt: Documentation Status (develop branch)
            :scale: 100%
            :target: https://gopca.readthedocs.org/en/develop
        
        
Keywords: unsupervised analysis gene expression data transcriptomics prior knowledge
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
