%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 * Tue May 30 2023 Python_Bot - 0.8.20-1 - Package Spec generated