Metadata-Version: 2.1
Name: epcy
Version: 0.2.4
Summary: Evaluattion of Predictive CapabilitY for ranking biomarker candidates.
Home-page: https://github.com/iric-soft/epcy
Author: IRIC_bioinfo, Eric Audemard
Author-email: pipy@iric.ca, eric.audemard@umontreal.ca
License: MIT
Keywords: Select predictive indicator
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Natural Language :: English
Requires-Python: >3.6
License-File: LICENSE
Requires-Dist: cython (>=0.29.14)
Requires-Dist: numpy (>=1.18.1)
Requires-Dist: pandas (>=0.25.3)
Requires-Dist: h5py (>=2.10.0)
Requires-Dist: scipy (>=1.4.1)
Requires-Dist: scikit-learn (>=0.22.1)
Requires-Dist: matplotlib (>=3.1.2)
Requires-Dist: numexpr (>=2.7.0)
Requires-Dist: seaborn (==0.10.0)

=============================================================================
EPCY :  Evaluation of Predictive CapabilitY for ranking biomarker candidates
=============================================================================

+------------------------------------------------------------------+-------------------------------------------------------------------+-------------------------------------------------------------------------------+
| .. image:: https://img.shields.io/badge/python-3.6-blue.svg      | .. image:: https://travis-ci.org/iric-soft/epcy.svg?branch=master | .. image:: https://codecov.io/gh/iric-soft/epcy/branch/master/graph/badge.svg |
|    :target: https://www.python.org/downloads/release/python-362/ |    :target: https://travis-ci.org/iric-soft/epcy                  |    :target: https://codecov.io/gh/iric-soft/epcy/                             |
+------------------------------------------------------------------+-------------------------------------------------------------------+-------------------------------------------------------------------------------+


-------
Citing:
-------
* Manuscript in preparation
* EPCY: Evaluation of Predictive CapabilitY for ranking biomarker gene candidates. Poster at ISMB ECCB 2019: https://f1000research.com/posters/8-1349

-------------
Introduction:
-------------

This tool was developed to Evaluate Predictive CapabilitY of each gene (feature) to become a predictive (bio)marker candidates.
Documentation is available `via Read the Docs <https://epcy.readthedocs.io/>`_.

-------------
Requirements:
-------------

* python3
* (Optional) virtualenv

--------
Install:
--------

Using pypi:
-----------

.. code:: shell

  pip install epcy

From source:
------------
.. code:: shell

  python3 -m venv $HOME/.virtualenvs/epcy
  source $HOME/.virtualenvs/epcy/bin/activate
  pip install pip setuptools --upgrade
  pip install wheel
  cd [your_epcy_folder]
  # If need it
  # CFLAGS=-std=c99 pip3 install numpy==1.17.0
  python3 setup.py install
  epcy -h

------
Usage:
------

General:
--------

After install:
**************

.. code:: shell

  epcy -h

From source:
************

.. code:: shell

  cd [your_epcy_folder]
  python3 -m epcy -h

Generic case:
-------------

* EPCY is design to work on any quantitative data, provided that values of each feature are comparable between each samples (normalized).
* To run a comparative analysis, `epcy pred` need two tabulated files:

  * A `matrix`_ of quantitative normalized data for each samples (column) with an "ID" column to identify each feature.
  * A `design`_ table which describe the comparison.

.. _matrix: https://github.com/iric-soft/epcy/blob/master/data/small_for_test/normalized_matrix.tsv
.. _design: https://github.com/iric-soft/epcy/blob/master/data/small_for_test/design.tsv

.. code:: shell

  # Run epcy on any normalized quantification data
  epcy pred -d ./data/small_for_test/design.tsv -m ./data/small_for_test/log_normalized_matrix.tsv -o ./data/small_for_test/EPCY_output

  # If your data are normalized, but require a log2 transforamtion, add --log
  epcy pred --log -d ./data/small_for_test/design.tsv -m ./data/small_for_test/normalized_matrix.tsv -o ./data/small_for_test/EPCY_output

  # If your data are not normalized and require a log2 transforamtion, add --norm --log
  epcy pred --norm --log -d ./data/small_for_test/design.tsv -m ./data/small_for_test/matrix.tsv -o ./data/small_for_test/EPCY_output

  # Different runs might show small variations.
  # To ensure reproducibility set a random seed, using --randomseed
  epcy pred -d ./data/small_for_test/design.tsv -m ./data/small_for_test/normalized_matrix.tsv -o ./data/small_for_test/EPCY_output --randomseed 42
  epcy pred -d ./data/small_for_test/design.tsv -m ./data/small_for_test/normalized_matrix.tsv -o ./data/small_for_test/EPCY_output2 --randomseed 42
  diff ./data/small_for_test/EPCY_output/predictive_capability.tsv ./data/small_for_test/EPCY_output2/predictive_capability.tsv


More documentation is available `via Read the Docs <https://epcy.readthedocs.io/>`_.
