blob: 17eb352e30526b4d00fcad4082d34140454460c5 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
|
%global _empty_manifest_terminate_build 0
Name: python-pyscenic
Version: 0.12.1
Release: 1
Summary: Python implementation of the SCENIC pipeline for transcription factor inference from single-cell transcriptomics experiments.
License: GPL-3.0+
URL: https://github.com/aertslab/pySCENIC
Source0: https://mirrors.aliyun.com/pypi/web/packages/d2/ea/109aa69d72b54ab78eb353ddc8b6ff7e78208b5d85b7d77f5d146b78681d/pyscenic-0.12.1.tar.gz
BuildArch: noarch
Requires: python3-ctxcore
Requires: python3-cytoolz
Requires: python3-multiprocessing-on-dill
Requires: python3-llvmlite
Requires: python3-numba
Requires: python3-attrs
Requires: python3-frozendict
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-numexpr
Requires: python3-cloudpickle
Requires: python3-dask
Requires: python3-distributed
Requires: python3-arboreto
Requires: python3-boltons
Requires: python3-setuptools
Requires: python3-pyyaml
Requires: python3-tqdm
Requires: python3-interlap
Requires: python3-umap-learn
Requires: python3-loompy
Requires: python3-networkx
Requires: python3-scipy
Requires: python3-fsspec
Requires: python3-requests
Requires: python3-aiohttp
Requires: python3-scikit-learn
%description
|buildstatus|_ |pypipackage|_ |docstatus|_
pySCENIC is a lightning-fast python implementation of the SCENIC_ pipeline (Single-Cell rEgulatory Network Inference and
Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from
single-cell RNA-seq data.
The pioneering work was done in R and results were published in Nature Methods [1]_.
A new and comprehensive description of this Python implementation of the SCENIC pipeline is available in Nature Protocols [4]_.
pySCENIC can be run on a single desktop machine but easily scales to multi-core clusters to analyze thousands of cells
in no time. The latter is achieved via the dask_ framework for distributed computing [2]_.
%package -n python3-pyscenic
Summary: Python implementation of the SCENIC pipeline for transcription factor inference from single-cell transcriptomics experiments.
Provides: python-pyscenic
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pyscenic
|buildstatus|_ |pypipackage|_ |docstatus|_
pySCENIC is a lightning-fast python implementation of the SCENIC_ pipeline (Single-Cell rEgulatory Network Inference and
Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from
single-cell RNA-seq data.
The pioneering work was done in R and results were published in Nature Methods [1]_.
A new and comprehensive description of this Python implementation of the SCENIC pipeline is available in Nature Protocols [4]_.
pySCENIC can be run on a single desktop machine but easily scales to multi-core clusters to analyze thousands of cells
in no time. The latter is achieved via the dask_ framework for distributed computing [2]_.
%package help
Summary: Development documents and examples for pyscenic
Provides: python3-pyscenic-doc
%description help
|buildstatus|_ |pypipackage|_ |docstatus|_
pySCENIC is a lightning-fast python implementation of the SCENIC_ pipeline (Single-Cell rEgulatory Network Inference and
Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from
single-cell RNA-seq data.
The pioneering work was done in R and results were published in Nature Methods [1]_.
A new and comprehensive description of this Python implementation of the SCENIC pipeline is available in Nature Protocols [4]_.
pySCENIC can be run on a single desktop machine but easily scales to multi-core clusters to analyze thousands of cells
in no time. The latter is achieved via the dask_ framework for distributed computing [2]_.
%prep
%autosetup -n pyscenic-0.12.1
%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-pyscenic -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.12.1-1
- Package Spec generated
|
