| Name | epcy JSON |
| Version |
0.2.6.4
JSON |
| download |
| home_page | None |
| Summary | Evaluation of Predictive CapabilitY |
| upload_time | 2024-06-13 15:25:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT License Copyright (c) 2017 Eric Audemard Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
density
predictive
gene
feature
kde
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
===========================================
EPCY : Evaluation of Predictive CapabilitY
===========================================
+------------------------------------------------------------+------------------------------------------------------------------+
| .. image:: https://zenodo.org/badge/197271057.svg | .. image:: https://img.shields.io/badge/python-3.11.5-blue.svg |
| :target: https://zenodo.org/doi/10.5281/zenodo.10407905 | :target: https://www.python.org/downloads/release/python-3115/|
+------------------------------------------------------------+------------------------------------------------------------------+
-------
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:
-------------
* python >= 3.11.5
--------
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]
pip install -e .
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/>`_.
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