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%global _empty_manifest_terminate_build 0
Name: python-SpaGCN
Version: 1.2.7
Release: 1
Summary: SpaGCN: Integrating gene expression and histology to identify spatial domains and spatially variable genes using graph convolutional networks
License: MIT License
URL: https://github.com/jianhuupenn/SpaGCN
Source0: https://mirrors.aliyun.com/pypi/web/packages/ad/34/5be61d402e7cbc99e49dd2db25508c336ea05b934faab8110c4b523b7082/SpaGCN-1.2.7.tar.gz
BuildArch: noarch
Requires: python3-igraph
Requires: python3-torch
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-scanpy
Requires: python3-anndata
Requires: python3-louvain
Requires: python3-scikit-learn
Requires: python3-numba
%description
# SpaGCN
## SpaGCN: Integrating gene expression and histology to identify spatial domains and spatially variable genes using graph convolutional networks
### Jian Hu,*, Xiangjie Li, Kyle Coleman, Amelia Schroeder, Nan Ma, David J. Irwin, Edward B. Lee, Russell T. Shinohara, Mingyao Li,*
SpaGCN is a graph convolutional network to integrate gene expression and histology to identify spatial domains and spatially variable genes. To jointly model all spots in a tissue slide, SpaGCN integrates information from gene expression, spatial locations and histological pixel intensities across spots into an undirected weighted graph. Each vertex in the graph contains gene expression information of a spot and the edge weight between two vertices quantifies their expression similarity that is driven by spatial dependency of their coordinates and the corresponding histology. To aggregate gene expression of each spot from its neighboring spots, SpaGCN utilizes a convolutional layer based on edge weights specified by the graph. The aggregated gene expression is then fed into a deep embedding clustering algorithm to cluster the spots into different spatial domains. After spatial domains are identified, genes that are enriched in each spatial domain can be detected by differential expression analysis between domains. SpaGCN is applicable to both in-situ transcriptomics with single-cell resolution (seqFISH, seqFISH+, MERFISH, STARmap, and FISSEQ) and spatial barcoding based transcriptomics (Spatial Transcriptomics , SLIDE-seq, SLIDE-seqV2, HDST, 10x Visium, DBiT-seq, Stero-seq, and PIXEL-seq) data.
For more info, please go to:
https://github.com/jianhuupenn/SpaGCN
%package -n python3-SpaGCN
Summary: SpaGCN: Integrating gene expression and histology to identify spatial domains and spatially variable genes using graph convolutional networks
Provides: python-SpaGCN
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-SpaGCN
# SpaGCN
## SpaGCN: Integrating gene expression and histology to identify spatial domains and spatially variable genes using graph convolutional networks
### Jian Hu,*, Xiangjie Li, Kyle Coleman, Amelia Schroeder, Nan Ma, David J. Irwin, Edward B. Lee, Russell T. Shinohara, Mingyao Li,*
SpaGCN is a graph convolutional network to integrate gene expression and histology to identify spatial domains and spatially variable genes. To jointly model all spots in a tissue slide, SpaGCN integrates information from gene expression, spatial locations and histological pixel intensities across spots into an undirected weighted graph. Each vertex in the graph contains gene expression information of a spot and the edge weight between two vertices quantifies their expression similarity that is driven by spatial dependency of their coordinates and the corresponding histology. To aggregate gene expression of each spot from its neighboring spots, SpaGCN utilizes a convolutional layer based on edge weights specified by the graph. The aggregated gene expression is then fed into a deep embedding clustering algorithm to cluster the spots into different spatial domains. After spatial domains are identified, genes that are enriched in each spatial domain can be detected by differential expression analysis between domains. SpaGCN is applicable to both in-situ transcriptomics with single-cell resolution (seqFISH, seqFISH+, MERFISH, STARmap, and FISSEQ) and spatial barcoding based transcriptomics (Spatial Transcriptomics , SLIDE-seq, SLIDE-seqV2, HDST, 10x Visium, DBiT-seq, Stero-seq, and PIXEL-seq) data.
For more info, please go to:
https://github.com/jianhuupenn/SpaGCN
%package help
Summary: Development documents and examples for SpaGCN
Provides: python3-SpaGCN-doc
%description help
# SpaGCN
## SpaGCN: Integrating gene expression and histology to identify spatial domains and spatially variable genes using graph convolutional networks
### Jian Hu,*, Xiangjie Li, Kyle Coleman, Amelia Schroeder, Nan Ma, David J. Irwin, Edward B. Lee, Russell T. Shinohara, Mingyao Li,*
SpaGCN is a graph convolutional network to integrate gene expression and histology to identify spatial domains and spatially variable genes. To jointly model all spots in a tissue slide, SpaGCN integrates information from gene expression, spatial locations and histological pixel intensities across spots into an undirected weighted graph. Each vertex in the graph contains gene expression information of a spot and the edge weight between two vertices quantifies their expression similarity that is driven by spatial dependency of their coordinates and the corresponding histology. To aggregate gene expression of each spot from its neighboring spots, SpaGCN utilizes a convolutional layer based on edge weights specified by the graph. The aggregated gene expression is then fed into a deep embedding clustering algorithm to cluster the spots into different spatial domains. After spatial domains are identified, genes that are enriched in each spatial domain can be detected by differential expression analysis between domains. SpaGCN is applicable to both in-situ transcriptomics with single-cell resolution (seqFISH, seqFISH+, MERFISH, STARmap, and FISSEQ) and spatial barcoding based transcriptomics (Spatial Transcriptomics , SLIDE-seq, SLIDE-seqV2, HDST, 10x Visium, DBiT-seq, Stero-seq, and PIXEL-seq) data.
For more info, please go to:
https://github.com/jianhuupenn/SpaGCN
%prep
%autosetup -n SpaGCN-1.2.7
%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-SpaGCN -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.7-1
- Package Spec generated
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