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https://github.com/pathwayforte/pathway-forte

A Python package for benchmarking pathway database with functional enrichment and classification methods
https://github.com/pathwayforte/pathway-forte

benchmarking bioinformatics databases machine-learning pathway-analysis pathway-enrichment-analysis systems-biology

Last synced: 5 months ago
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A Python package for benchmarking pathway database with functional enrichment and classification methods

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README 

        
PathwayForte |build| |docs| |coverage| |zenodo|

===============================================

A Python package for benchmarking pathway databases with functional enrichment and prediction methods

tasks.



If you find ``pathway_forte`` useful for your work, please consider citing:



.. [1] Mubeen, S., *et al* (2019). `The Impact of Pathway Database Choice on

       Statistical Enrichment Analysis and Predictive Modeling

       `_. *Front. Genet.*, 10:1203.



Installation |pypi_version| |python_versions| |pypi_license|

------------------------------------------------------------

``pathway_forte`` can be installed from `PyPI `_

with the following command in your terminal:



.. code-block:: sh



    $ python3 -m pip install pathway_forte



The latest code can be installed from `GitHub `_

with:



.. code-block:: sh



    $ python3 -m pip install git+https://github.com/pathwayforte/pathway-forte.git



For developers, the code can be installed with:



.. code-block:: sh



    $ git clone https://github.com/pathwayforte/pathway-forte.git

    $ cd pathway-forte

    $ python3 -m pip install -e .



Main Commands

-------------

The table below lists the main commands of PathwayForte.



+------------+--------------------------------+

| Command    | Action                         |

+============+================================+

| datasets   | Lists of Cancer Datasets       |

+------------+--------------------------------+

| export     | Export Gene Sets using ComPath |

+------------+--------------------------------+

| ora        | List of ORA Analyses           |

+------------+--------------------------------+

| fcs        | List of FCS Analyses           |

+------------+--------------------------------+

| prediction | List of Prediction Methods     |

+------------+--------------------------------+



Functional Enrichment Methods

-----------------------------

- **ora**. Lists Over-Representation Analyses (e.g., one-tailed hyper-geometric test).

- **fcs**. Lists Functional Class Score Analyses such as GSEA and ssGSEA using

  `GSEAPy `_.



Prediction Methods

------------------

``pathway_forte`` enables three classification methods (i.e., binary classification, training SVMs for

multi-classification tasks, or survival analysis) using individualized pathway activity scores. The scores can be

calculated from any pathway with a variety of tools (see [2]_) using any pathway database that enables to export its

gene sets.



- **binary**. Trains an elastic net model for a binary classification task (e.g., tumor vs. normal patients). The

  training is conducted using a nested cross validation approach (the number of cross validation in both loops can be

  selected). The model used can be easily changed since most of the models in

  `scikit-learn `_ (the machine learning library used by this package) required the same

  input.

- **subtype**. Trains a SVM model for a multi-class classification task (e.g., predict tumor subtypes). The training is

  conducted using a nested cross validation approach (the number of cross validation in both loops can be selected).

  Similarly as the previous classification task, other models can quickly be implemented.

- **survival**. Trains a Cox's proportional hazard's model with elastic net penalty. The training is conducted using a

  nested cross validation approach with a grid search in the inner loop. This analysis requires pathway activity

  scores, patient classes and lifetime patient information.



Other

-----

- **export**. Export GMT files with current gene sets for the pathway databases included in ComPath [3]_.

- **datasets**. Lists the TCGA data sets [4]_ that are ready to run in ``pathway_forte``.



References

----------

.. [2] Lim, S., *et al.* (2018). `Comprehensive and critical evaluation of individualized pathway activity measurement

       tools on pan-cancer data `_. *Briefings in bioinformatics*, bby125.

.. [3] Domingo-Fernández, D., *et al.* (2018). `ComPath: An ecosystem for exploring, analyzing, and curating mappings

       across pathway databases `_. *npj Syst Biol Appl.*, 4(1):43.

.. [4] Weinstein, J. N., *et al.* (2013). `The cancer genome atlas pan-cancer analysis project

       `_. *Nature genetics*, 45(10), 1113.



License

-------

The Pathway Forte logo is derived from `"Muscle Fat" `_ by Lorc, used under CC BY 3.0.



Disclaimer

-----------

PathForte is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or

guarantee of maintenance, support, or back-up of data.



.. |build| image:: https://travis-ci.com/pathwayforte/pathway-forte.svg?branch=master

    :target: https://travis-ci.com/pathwayforte/pathway-forte

    :alt: Build Status



.. |docs| image:: http://readthedocs.org/projects/pathwayforte/badge/?version=latest

    :target: https://pathwayforte.readthedocs.io/en/latest/

    :alt: Documentation Status



.. |coverage| image:: https://codecov.io/gh/pathwayforte/pathway-forte/coverage.svg?branch=master

    :target: https://codecov.io/gh/pathwayforte/pathway-forte?branch=master

    :alt: Coverage Status



.. |python_versions| image:: https://img.shields.io/pypi/pyversions/pathway_forte.svg

    :target: https://pypi.org/project/pathway-forte

    :alt: Stable Supported Python Versions



.. |pypi_version| image:: https://img.shields.io/pypi/v/pathway_forte.svg

    :target: https://pypi.org/project/pathway-forte

    :alt: Current version on PyPI



.. |pypi_license| image:: https://img.shields.io/pypi/l/pathway_forte.svg

    :target: https://github.com/pathwayforte/pathway-forte/blob/master/LICENSE

    :alt: Apache-2.0



.. |zenodo| image:: https://zenodo.org/badge/178654585.svg

    :target: https://zenodo.org/badge/latestdoi/178654585