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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.

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']

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

Please refer to the XL-mHG User Manual.

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 (c) 2015-2019 Florian Wagner

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