=========================================== 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/>_.