%global _empty_manifest_terminate_build 0 Name: python-scvelo Version: 0.2.5 Release: 1 Summary: RNA velocity generalized through dynamical modeling License: BSD URL: https://github.com/theislab/scvelo Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d7/ca/6669ad5c6765493ea9db0fb54b766d043b09db5a146f178c106b7162d1ef/scvelo-0.2.5.tar.gz BuildArch: noarch Requires: python3-typing-extensions Requires: python3-anndata Requires: python3-scanpy Requires: python3-loompy Requires: python3-umap-learn Requires: python3-numba Requires: python3-numpy Requires: python3-scipy Requires: python3-pandas Requires: python3-scikit-learn Requires: python3-matplotlib Requires: python3-black Requires: python3-hnswlib Requires: python3-hypothesis Requires: python3-flake8 Requires: python3-isort Requires: python3-louvain Requires: python3-magic-impute Requires: python3-pre-commit Requires: python3-pybind11 Requires: python3-pytest Requires: python3-pytest-cov Requires: python3-igraph Requires: python3-scanpy Requires: python3-setuptools Requires: python3-setuptools-scm Requires: python3-typing-extensions Requires: python3-importlib-metadata Requires: python3-sphinx-rtd-theme Requires: python3-sphinx-autodoc-typehints Requires: python3-Jinja2 Requires: python3-ipykernel Requires: python3-sphinx Requires: python3-nbsphinx Requires: python3-pybind11 Requires: python3-hnswlib Requires: python3-igraph Requires: python3-louvain %description **scVelo** is a scalable toolkit for RNA velocity analysis in single cells, based on `Bergen et al. (Nature Biotech, 2020) `_. RNA velocity enables the recovery of directed dynamic information by leveraging splicing kinetics. scVelo generalizes the concept of RNA velocity (`La Manno et al., Nature, 2018 `_) by relaxing previously made assumptions with a stochastic and a dynamical model that solves the full transcriptional dynamics. It thereby adapts RNA velocity to widely varying specifications such as non-stationary populations. scVelo is compatible with scanpy_ and hosts efficient implementations of all RNA velocity models. scVelo's key applications ^^^^^^^^^^^^^^^^^^^^^^^^^ - estimate RNA velocity to study cellular dynamics. - identify putative driver genes and regimes of regulatory changes. - infer a latent time to reconstruct the temporal sequence of transcriptomic events. - estimate reaction rates of transcription, splicing and degradation. - use statistical tests, e.g., to detect different kinetics regimes. scVelo has, for instance, recently been used to study immune response in COVID-19 patients and dynamic processes in human lung regeneration. Find out more in this list of `application examples `_. Latest news ^^^^^^^^^^^ - Aug/2021: `Perspectives paper out in MSB `_ - Feb/2021: scVelo goes multi-core - Dec/2020: Cover of `Nature Biotechnology `_ - Nov/2020: Talk at `Single Cell Biology `_ - Oct/2020: `Helmholtz Best Paper Award `_ - Oct/2020: Map cell fates with `CellRank `_ - Sep/2020: Talk at `Single Cell Omics `_ - Aug/2020: `scVelo out in Nature Biotech `_ References ^^^^^^^^^^ La Manno *et al.* (2018), RNA velocity of single cells, `Nature `_. Bergen *et al.* (2020), Generalizing RNA velocity to transient cell states through dynamical modeling, `Nature Biotech `_. Bergen *et al.* (2021), RNA velocity - current challenges and future perspectives, `Molecular Systems Biology `_. Support ^^^^^^^ Found a bug or would like to see a feature implemented? Feel free to submit an `issue `_. Have a question or would like to start a new discussion? Head over to `GitHub discussions `_. In either case, you can also always send us an `email `_. Your help to improve scVelo is highly appreciated. For further information visit `scvelo.org `_. %package -n python3-scvelo Summary: RNA velocity generalized through dynamical modeling Provides: python-scvelo BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-scvelo **scVelo** is a scalable toolkit for RNA velocity analysis in single cells, based on `Bergen et al. (Nature Biotech, 2020) `_. RNA velocity enables the recovery of directed dynamic information by leveraging splicing kinetics. scVelo generalizes the concept of RNA velocity (`La Manno et al., Nature, 2018 `_) by relaxing previously made assumptions with a stochastic and a dynamical model that solves the full transcriptional dynamics. It thereby adapts RNA velocity to widely varying specifications such as non-stationary populations. scVelo is compatible with scanpy_ and hosts efficient implementations of all RNA velocity models. scVelo's key applications ^^^^^^^^^^^^^^^^^^^^^^^^^ - estimate RNA velocity to study cellular dynamics. - identify putative driver genes and regimes of regulatory changes. - infer a latent time to reconstruct the temporal sequence of transcriptomic events. - estimate reaction rates of transcription, splicing and degradation. - use statistical tests, e.g., to detect different kinetics regimes. scVelo has, for instance, recently been used to study immune response in COVID-19 patients and dynamic processes in human lung regeneration. Find out more in this list of `application examples `_. Latest news ^^^^^^^^^^^ - Aug/2021: `Perspectives paper out in MSB `_ - Feb/2021: scVelo goes multi-core - Dec/2020: Cover of `Nature Biotechnology `_ - Nov/2020: Talk at `Single Cell Biology `_ - Oct/2020: `Helmholtz Best Paper Award `_ - Oct/2020: Map cell fates with `CellRank `_ - Sep/2020: Talk at `Single Cell Omics `_ - Aug/2020: `scVelo out in Nature Biotech `_ References ^^^^^^^^^^ La Manno *et al.* (2018), RNA velocity of single cells, `Nature `_. Bergen *et al.* (2020), Generalizing RNA velocity to transient cell states through dynamical modeling, `Nature Biotech `_. Bergen *et al.* (2021), RNA velocity - current challenges and future perspectives, `Molecular Systems Biology `_. Support ^^^^^^^ Found a bug or would like to see a feature implemented? Feel free to submit an `issue `_. Have a question or would like to start a new discussion? Head over to `GitHub discussions `_. In either case, you can also always send us an `email `_. Your help to improve scVelo is highly appreciated. For further information visit `scvelo.org `_. %package help Summary: Development documents and examples for scvelo Provides: python3-scvelo-doc %description help **scVelo** is a scalable toolkit for RNA velocity analysis in single cells, based on `Bergen et al. (Nature Biotech, 2020) `_. RNA velocity enables the recovery of directed dynamic information by leveraging splicing kinetics. scVelo generalizes the concept of RNA velocity (`La Manno et al., Nature, 2018 `_) by relaxing previously made assumptions with a stochastic and a dynamical model that solves the full transcriptional dynamics. It thereby adapts RNA velocity to widely varying specifications such as non-stationary populations. scVelo is compatible with scanpy_ and hosts efficient implementations of all RNA velocity models. scVelo's key applications ^^^^^^^^^^^^^^^^^^^^^^^^^ - estimate RNA velocity to study cellular dynamics. - identify putative driver genes and regimes of regulatory changes. - infer a latent time to reconstruct the temporal sequence of transcriptomic events. - estimate reaction rates of transcription, splicing and degradation. - use statistical tests, e.g., to detect different kinetics regimes. scVelo has, for instance, recently been used to study immune response in COVID-19 patients and dynamic processes in human lung regeneration. Find out more in this list of `application examples `_. Latest news ^^^^^^^^^^^ - Aug/2021: `Perspectives paper out in MSB `_ - Feb/2021: scVelo goes multi-core - Dec/2020: Cover of `Nature Biotechnology `_ - Nov/2020: Talk at `Single Cell Biology `_ - Oct/2020: `Helmholtz Best Paper Award `_ - Oct/2020: Map cell fates with `CellRank `_ - Sep/2020: Talk at `Single Cell Omics `_ - Aug/2020: `scVelo out in Nature Biotech `_ References ^^^^^^^^^^ La Manno *et al.* (2018), RNA velocity of single cells, `Nature `_. Bergen *et al.* (2020), Generalizing RNA velocity to transient cell states through dynamical modeling, `Nature Biotech `_. Bergen *et al.* (2021), RNA velocity - current challenges and future perspectives, `Molecular Systems Biology `_. Support ^^^^^^^ Found a bug or would like to see a feature implemented? Feel free to submit an `issue `_. Have a question or would like to start a new discussion? Head over to `GitHub discussions `_. In either case, you can also always send us an `email `_. Your help to improve scVelo is highly appreciated. For further information visit `scvelo.org `_. %prep %autosetup -n scvelo-0.2.5 %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-scvelo -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 0.2.5-1 - Package Spec generated