XL-mHG lite: A light implementation of the Semiparametric Enrichment Test
Project description
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. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
History
1.1.1 (2024-06-30)
xlmhglite now runs on numpy version 2 and above.
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.
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