Metadata-Version: 2.1
Name: HAllA
Version: 0.8.30
Summary: HAllA: Hierarchical All-against All Association Testing
Home-page: https://github.com/biobakery/halla
Author: HAllA Development Team
Author-email: halla-users@googlegroups.com
License: MIT
Keywords: halla,association testing
Platform: Linux
Platform: MacOS
Classifier: Programming Language :: Python
Classifier: Operating System :: MacOS
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Dist: jenkspy (>=0.1.5)
Requires-Dist: matplotlib (>=3.5.3)
Requires-Dist: numpy (>=1.19.0)
Requires-Dist: pandas (>=1.0.5)
Requires-Dist: PyYAML (>=5.4)
Requires-Dist: rpy2 (>=3.3.5)
Requires-Dist: scikit-learn (>=0.23.1)
Requires-Dist: scipy (>=1.5.1)
Requires-Dist: seaborn (>=0.10.1)
Requires-Dist: statsmodels (>=0.11.1)
Requires-Dist: tqdm (>=4.50.2)

Given two high-dimensional 'omics datasets X and Y (continuous and/or categorical features) from the same n biosamples, HAllA (Hierarchical All-against-All Association Testing) discovers densely-associated blocks of features in the X vs. Y association matrix where: 1) each block is defined as all associations between features in a subtree of X hierarchy and features in a subtree of Y hierarchy and 2) a block is densely associated if (1 - FNR)% of pairwise associations are FDR significant (FNR is the pre-defined expected false negative rate)


