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%global _empty_manifest_terminate_build 0
Name: python-PhenoGraph
Version: 1.5.7
Release: 1
Summary: Graph-based clustering for high-dimensional single-cell data
License: LICENSE
URL: https://github.com/dpeerlab/PhenoGraph.git
Source0: https://mirrors.aliyun.com/pypi/web/packages/75/97/c1077df94cb0e1ebd201d67e52c438810fdaa393dc4c663b3bc651e497b8/PhenoGraph-1.5.7.tar.gz
BuildArch: noarch
Requires: python3-leidenalg
Requires: python3-setuptools
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-scikit-learn
Requires: python3-psutil
%description
[PhenoGraph](http://www.cell.com/cell/abstract/S0092-8674(15)00637-6) is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") representing phenotypic similarities between cells and then identifying communities in this graph.
This software package includes compiled binaries that run community detection based on C++ code written by E. Lefebvre and J.-L. Guillaume in 2008 (["Louvain method"](https://sites.google.com/site/findcommunities/)). The code has been altered to interface more efficiently with the Python code here. It should work on reasonably current Linux, Mac and Windows machines.
To install PhenoGraph, simply run the setup script:
pip install PhenoGraph
Expected use is within a script or interactive kernel running Python `3.x`. Data are expected to be passed as a `numpy.ndarray`. When applicable, the code uses CPU multicore parallelism via `multiprocessing`.
To run basic clustering:
import phenograph
communities, graph, Q = phenograph.cluster(data)
For a dataset of *N* rows, `communities` will be a length *N* vector of integers specifying a community assignment for each row in the data. Any rows assigned `-1` were identified as *outliers* and should not be considered as a member of any community. `graph` is a *N* x *N* `scipy.sparse` matrix representing the weighted graph used for community detection.
`Q` is the modularity score for `communities` as applied to `graph`.
If you use PhenoGraph in work you publish, please cite our publication:
@article{Levine_PhenoGraph_2015,
doi = {10.1016/j.cell.2015.05.047},
url = {http://dx.doi.org/10.1016/j.cell.2015.05.047},
year = {2015},
month = {jul},
publisher = {Elsevier {BV}},
volume = {162},
number = {1},
pages = {184--197},
author = {Jacob H. Levine and Erin F. Simonds and Sean C. Bendall and Kara L. Davis and El-ad D. Amir and Michelle D. Tadmor and Oren Litvin and Harris G. Fienberg and Astraea Jager and Eli R. Zunder and Rachel Finck and Amanda L. Gedman and Ina Radtke and James R. Downing and Dana Pe'er and Garry P. Nolan},
title = {Data-Driven Phenotypic Dissection of {AML} Reveals Progenitor-like Cells that Correlate with Prognosis},
journal = {Cell}
}
%package -n python3-PhenoGraph
Summary: Graph-based clustering for high-dimensional single-cell data
Provides: python-PhenoGraph
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-PhenoGraph
[PhenoGraph](http://www.cell.com/cell/abstract/S0092-8674(15)00637-6) is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") representing phenotypic similarities between cells and then identifying communities in this graph.
This software package includes compiled binaries that run community detection based on C++ code written by E. Lefebvre and J.-L. Guillaume in 2008 (["Louvain method"](https://sites.google.com/site/findcommunities/)). The code has been altered to interface more efficiently with the Python code here. It should work on reasonably current Linux, Mac and Windows machines.
To install PhenoGraph, simply run the setup script:
pip install PhenoGraph
Expected use is within a script or interactive kernel running Python `3.x`. Data are expected to be passed as a `numpy.ndarray`. When applicable, the code uses CPU multicore parallelism via `multiprocessing`.
To run basic clustering:
import phenograph
communities, graph, Q = phenograph.cluster(data)
For a dataset of *N* rows, `communities` will be a length *N* vector of integers specifying a community assignment for each row in the data. Any rows assigned `-1` were identified as *outliers* and should not be considered as a member of any community. `graph` is a *N* x *N* `scipy.sparse` matrix representing the weighted graph used for community detection.
`Q` is the modularity score for `communities` as applied to `graph`.
If you use PhenoGraph in work you publish, please cite our publication:
@article{Levine_PhenoGraph_2015,
doi = {10.1016/j.cell.2015.05.047},
url = {http://dx.doi.org/10.1016/j.cell.2015.05.047},
year = {2015},
month = {jul},
publisher = {Elsevier {BV}},
volume = {162},
number = {1},
pages = {184--197},
author = {Jacob H. Levine and Erin F. Simonds and Sean C. Bendall and Kara L. Davis and El-ad D. Amir and Michelle D. Tadmor and Oren Litvin and Harris G. Fienberg and Astraea Jager and Eli R. Zunder and Rachel Finck and Amanda L. Gedman and Ina Radtke and James R. Downing and Dana Pe'er and Garry P. Nolan},
title = {Data-Driven Phenotypic Dissection of {AML} Reveals Progenitor-like Cells that Correlate with Prognosis},
journal = {Cell}
}
%package help
Summary: Development documents and examples for PhenoGraph
Provides: python3-PhenoGraph-doc
%description help
[PhenoGraph](http://www.cell.com/cell/abstract/S0092-8674(15)00637-6) is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") representing phenotypic similarities between cells and then identifying communities in this graph.
This software package includes compiled binaries that run community detection based on C++ code written by E. Lefebvre and J.-L. Guillaume in 2008 (["Louvain method"](https://sites.google.com/site/findcommunities/)). The code has been altered to interface more efficiently with the Python code here. It should work on reasonably current Linux, Mac and Windows machines.
To install PhenoGraph, simply run the setup script:
pip install PhenoGraph
Expected use is within a script or interactive kernel running Python `3.x`. Data are expected to be passed as a `numpy.ndarray`. When applicable, the code uses CPU multicore parallelism via `multiprocessing`.
To run basic clustering:
import phenograph
communities, graph, Q = phenograph.cluster(data)
For a dataset of *N* rows, `communities` will be a length *N* vector of integers specifying a community assignment for each row in the data. Any rows assigned `-1` were identified as *outliers* and should not be considered as a member of any community. `graph` is a *N* x *N* `scipy.sparse` matrix representing the weighted graph used for community detection.
`Q` is the modularity score for `communities` as applied to `graph`.
If you use PhenoGraph in work you publish, please cite our publication:
@article{Levine_PhenoGraph_2015,
doi = {10.1016/j.cell.2015.05.047},
url = {http://dx.doi.org/10.1016/j.cell.2015.05.047},
year = {2015},
month = {jul},
publisher = {Elsevier {BV}},
volume = {162},
number = {1},
pages = {184--197},
author = {Jacob H. Levine and Erin F. Simonds and Sean C. Bendall and Kara L. Davis and El-ad D. Amir and Michelle D. Tadmor and Oren Litvin and Harris G. Fienberg and Astraea Jager and Eli R. Zunder and Rachel Finck and Amanda L. Gedman and Ina Radtke and James R. Downing and Dana Pe'er and Garry P. Nolan},
title = {Data-Driven Phenotypic Dissection of {AML} Reveals Progenitor-like Cells that Correlate with Prognosis},
journal = {Cell}
}
%prep
%autosetup -n PhenoGraph-1.5.7
%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-PhenoGraph -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.5.7-1
- Package Spec generated
|
