automatic import of python-SpaGCNopeneuler20.03

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+/SpaGCN-1.2.7.tar.gz
diff --git a/python-spagcn.spec b/python-spagcn.spec
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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
diff --git a/sources b/sources
new file mode 100644
index 0000000..86066cf
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f9e293576b125fef07c0741b2f4af3dc  SpaGCN-1.2.7.tar.gz