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PathCORE-T-1.0.2
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مشاهده بیشترتوضیحات
Python 3 implementation of PathCORE-T analysis methods
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | PathCORE-T-1.0.2 |
| نام | PathCORE-T |
| نسخه کتابخانه | 1.0.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Greene Lab |
| ایمیل نویسنده | team@greenelab.com |
| آدرس صفحه اصلی | https://github.com/greenelab/PathCORE-T |
| آدرس اینترنتی | https://pypi.org/project/PathCORE-T/ |
| مجوز | BSD-3-Clause |
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
نحوه نصب
نصب پکیج whl PathCORE-T-1.0.2:
pip install PathCORE-T-1.0.2.whl
نصب پکیج tar.gz PathCORE-T-1.0.2:
pip install PathCORE-T-1.0.2.tar.gz