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%global _empty_manifest_terminate_build 0
Name: python-HAllA
Version: 0.8.20
Release: 1
Summary: HAllA: Hierarchical All-against All Association Testing
License: MIT
URL: https://github.com/biobakery/halla
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4f/c1/a5d48566d0b415b38e58a2c6d8b9e9f6d2d20201b489138fc51657ccaca5/HAllA-0.8.20.tar.gz
BuildArch: noarch
Requires: python3-jenkspy
Requires: python3-matplotlib
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-PyYAML
Requires: python3-rpy2
Requires: python3-scikit-learn
Requires: python3-scipy
Requires: python3-seaborn
Requires: python3-sklearn
Requires: python3-statsmodels
Requires: python3-tqdm
%description
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)
%package -n python3-HAllA
Summary: HAllA: Hierarchical All-against All Association Testing
Provides: python-HAllA
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-HAllA
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)
%package help
Summary: Development documents and examples for HAllA
Provides: python3-HAllA-doc
%description help
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)
%prep
%autosetup -n HAllA-0.8.20
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-HAllA -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.20-1
- Package Spec generated
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