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pathcore-t

Methods to build a network of pathway co-occurrence relationships out of expression signatures extracted from transcriptomic compendia.

https://github.com/greenelab/pathcore-t

Science Score: 13.0%

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Keywords

analysis gene-expression methodology supplement
Last synced: 7 months ago · JSON representation

Repository

Methods to build a network of pathway co-occurrence relationships out of expression signatures extracted from transcriptomic compendia.

Basic Info
  • Host: GitHub
  • Owner: greenelab
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 21.5 KB
Statistics
  • Stars: 4
  • Watchers: 3
  • Forks: 3
  • Open Issues: 1
  • Releases: 0
Topics
analysis gene-expression methodology supplement
Created almost 9 years ago · Last pushed over 8 years ago
Metadata Files
Readme License

README.rst 

PathCORE-T
----------
Python 3 implementation of methods described in
`Chen et al.'s 2017 PathCORE-T paper `_.

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 `_.

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 `_
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 `_.

Specifically, `this Jupyter notebook `_
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. `_]

===========
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. `_]

============================
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. `_]

Acknowledgements
----------------
This work was supported by the Penn Institute for Bioinformatics

Owner

  • Name: Greene Laboratory
  • Login: greenelab
  • Kind: organization
  • Email: casey@greenelab.com
  • Location: Aurora, CO

Greene Laboratory at the University of Colorado Anschutz

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  • Total packages: 1
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pypi.org: pathcore-t

Python 3 implementation of PathCORE-T analysis methods

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 7 Last month
Rankings
Dependent packages count: 10.0%
Forks count: 16.9%
Dependent repos count: 21.7%
Average: 22.6%
Stargazers count: 23.1%
Downloads: 41.3%
Maintainers (1)
kathychen
Last synced: 7 months ago

Dependencies

setup.py pypi
  • crosstalk-correction *
  • numpy *
  • pandas *
  • scipy *
  • statsmodels *