Metadata-Version: 1.1
Name: PathCORE-T
Version: 1.0.2
Summary: Python 3 implementation of PathCORE-T analysis methods
Home-page: https://github.com/greenelab/PathCORE-T
Author: Greene Lab
Author-email: team@greenelab.com
License: BSD-3-Clause
Description: PathCORE-T
        ----------
        Python 3 implementation of methods described in
        `Chen et al.'s 2017 PathCORE-T paper <https://doi.org/10.1101/147645>`_.
        
        Note that this software was renamed from PathCORE to PathCORE-T in Oct 2017.
        The T specifies that pathway co-occurrence relationships are identified using
        features extracted from **transcriptomic** data. 
        The module itself is still named `pathcore` to maintain backwards
        compatibility for users of the original PathCORE software package. 
        
        This code has been tested on Python 3.5.
        The documentation for the modules in the package can be
        `accessed here <http://pathcore-demo.herokuapp.com/static/data/docs_pathcore/index.html>`_.
        
        Installation
        ----------------
        To install the current PyPI version (recommended), run::
        
            pip install PathCORE-T
        
        For the latest GitHub version, run::
        
            pip install git+https://github.com/greenelab/PathCORE-T.git#egg=PathCORE-T
        
        Examples
        ---------
        We recommend that users of the PathCORE-T software begin by reviewing the
        examples in the `PathCORE-T-analysis <https://github.com/greenelab/PathCORE-T-analysis>`_
        repository. The analysis repository contains shell scripts and wrapper
        analysis scripts that demonstrate how to run the methods in this package
        on features constructed from a broad compendium according to the 
        `workflow we describe in our paper <https://github.com/greenelab/PathCORE-T-analysis#the-pathcore-analysis-workflow>`_.
        
        Specifically, `this Jupyter notebook <https://github.com/greenelab/PathCORE-T-analysis/blob/master/jupyter-notebooks/Supplemental_PAO1_FastICA_example.ipynb>`_
        is a simple example of the workflow and a great place to start.
        
        Package contents
        ----------------
        
        =====================================
        feature_pathway_overrepresentation.py
        =====================================
        The methods in this module are used to identify the pathways
        overrepresented in features extracted from a transcriptomic dataset
        of genes-by-samples. Features must preserve the genes in the dataset
        and assign weights to these genes based on some distribution.
        [`feature_pathway_overrepresentation documentation. <http://pathcore-demo.herokuapp.com/static/data/docs_pathcore/source/pathcore.html#module-pathcore.feature_pathway_overrepresentation>`_]
        
        ===========
        network.py
        ===========
        Contains the data structure ``CoNetwork`` that stores information
        about the pathway co-occurrence network. The output from
        a pathway enrichment analysis in ``feature_pathway_overrepresentation.py``
        serves as input into the ``CoNetwork`` constructor.
        [`CoNetwork documentation. <http://pathcore-demo.herokuapp.com/static/data/docs_pathcore/source/pathcore.html#module-pathcore.network>`_]
        
        ============================
        network_permutation_test.py
        ============================
        The methods in this module are used to filter the constructed
        co-occurence network. We implement a permutation test that evaluates
        and removes edges (pathway-pathway relationships) in the network
        that cannot be distinguished from a null model of random associations.
        The null model is created by generating *N* permutations of the network.
        [`network_permutation_test documentation. <http://pathcore-demo.herokuapp.com/static/data/docs_pathcore/source/pathcore.html#module-pathcore.network_permutation_test>`_]
        
        Acknowledgements
        ----------------
        This work was supported by the Penn Institute for Bioinformatics
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
