xlmhglite 1.1.0

XL-mHG lite: A light implementation of the Semiparametric Enrichment Test

XL-mHG Lite

Useful links: Documentation | Source code | Bug reports | | | | |

xlmhg is an efficient Python/Cython implementation of the semiparametric XL-mHG test for enrichment in ranked lists. The XL-mHG test is an extension of the nonparametric mHG test, which was developed by Dr. Zohar Yakhini and colleagues.

xlmhglite is a fork of the original xlmhg package (which is unfortunately no longer being maintained). This fork was updated to support modern Python versions (Python >=3.8), fix bugs in the original implementation, and reduce the mandatory dependencies of the project to a minimum. To that end, the plotting functionality of xlmhg is not part of the core xlmhglite package, instead being an optional requirement.

Installation

To install the core (“lite”) version of xlmhglite: .. code-block:: bash

$ pip install xlmhglite

To install the complete version of xlmhglite (including the plotting functionality): .. code-block:: bash

$ pip install xlmhglite[‘all’]

Getting started

The xlmhglite package provides two functions (one simple and more more advanced) for performing XL-mHG tests. These functions are documented in the User Manual. Here’s a quick example using the “simple” test function:

import xlmhglite
stat, cutoff, pval = xlmhglite.xlmhg_test(v, X, L)

Where: v is the ranked list of 0’s and 1’s, represented by a NumPy array of integers, X and L are the XL-mHG parameters, and the return values have the following meanings:

• stat: The XL-mHG test statistic

• cutoff: The cutoff at which XL-mHG test statistic was attained

• pval: The XL-mHG p-value

XL-mHG Lite Documentation

Please refer to the XL-mHG User Manual.

Citing XL-mHG

If you use the XL-mHG test in your research, please cite Eden et al. (PLoS Comput Biol, 2007) and Wagner (PLoS One, 2015).

Copyright and License

Copyright (c) 2015-2019 Florian Wagner

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.

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* Neither the name of the copyright holder nor the names of its
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this software without specific prior written permission.

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History

1.1.0 (2023-06-11)

This version improves clarity of warning messages and addresses some additional bugs. Moreover, the project has been transitioned to use pyproject.toml and setup.cfg, and old code was cleaned up for better maintainability.

Changed

• Warning messages regarding failed import of the cython module were made more informative.

• Transitioned the project to use pyproject.toml and setup.cfg, and cleaned up legacy code from setup.py.

Fixed

• Fixed bug where calculating enrichment scores using the pure Python implementation would raise an AttributeError.

• Fixed bug where the pure Python implementation would raise an ImportError if numba is not already installed on the system.

1.0.1 (2023-06-11)

Minor patch addressing installation issues.

1.0.0 (2023-06-10)

First stable release.