automatic import of python-pygnaopeneuler20.03

3 files changed, 467 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
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+/pygna-3.3.1.tar.gz
diff --git a/python-pygna.spec b/python-pygna.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-pygna
+Version:	3.3.1
+Release:	1
+Summary:	Geneset Network Analysis
+License:	MIT
+URL:		https://github.com/stracquadaniolab/pygna
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/82/63/56e835cd86a4070e90a7d5d6e59bda2eec44cf36393958a2722b685790cb/pygna-3.3.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-pandas
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-matplotlib
+Requires:	python3-pyyaml
+Requires:	python3-tables
+Requires:	python3-seaborn
+Requires:	python3-palettable
+Requires:	python3-networkx
+Requires:	python3-statsmodels
+Requires:	python3-argh
+Requires:	python3-mygene
+
+%description
+# PyGNA: a Python framework for geneset network analysis
+
+![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/stracquadaniolab/pygna?style=flat-square)
+[![Anaconda-Server Badge](https://anaconda.org/stracquadaniolab/pygna/badges/version.svg)](https://anaconda.org/stracquadaniolab/pygna)
+![Build](https://github.com/stracquadaniolab/pygna/workflows/Build/badge.svg)
+![Release](https://github.com/stracquadaniolab/pygna/workflows/Release/badge.svg)
+
+PyGNA is a framework for statistical network analysis of high-throughput experiments. It can
+be used both as a standalone command line application or it can be used as API
+to develop custom analyses.
+
+For an overview of PyGNA functionalities check the infographic below or dive into our [Getting started](#getting-started) tour.
+
+![Infographic](docs/pygna_infographic-01.png)
+
+## Installation
+
+The easiest and fastest way to install `pygna` using `conda`:
+
+    $ conda install -c stracquadaniolab -c bioconda -c conda-forge pygna
+
+Alternatively you can install it through `pip`:
+
+    $ pip install pygna
+
+We also provide a docker image installation with the latest version of PyGNA.
+It can be easily executed from the command line from DockerHub:
+
+    $ docker run stracquadaniolab/pygna/pygna:latest
+
+or GitHub Packages:
+
+    $ docker run docker.pkg.github.com/stracquadaniolab/pygna/pygna:latest
+
+
+which will show the PyGNA command line help.
+
+## Getting started
+
+A typical `pygna` analysis consists of 3 steps:
+
+1. Generate the RWR and SP matrices for the network you are using ( once they are generated, you won't need to repeat the same step again)
+2. Make sure that the input genesets are in the right format. If a network uses entrez ID, and your file is in HUGO symbols, use the pygna utility for the name conversion.
+3. Run the analysis you are interested into.
+4. Once you have the output tables, you can choose to visualize one or more plots.
+
+Otherwise you can check our [snakemake workflow](https://github.com/stracquadaniolab/workflow-pygna) for the full geneset analysis;
+our workflow contains sample data that you can use to familiarize with our software.
+
+
+The examples below show some basic analysis that can be carried out with pygna.
+
+### Example 1: Running pygna GNT analysis
+
+Running `pygna` on this input as follows:
+
+    $ cd ./your-path/min-working-example/
+
+    $ pygna build-rwr-diffusion barabasi.interactome.tsv --output-file interactome_RWR.hdf5
+
+    $ pygna test-topology-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_topology_rwr.csv --number-of-permutations 1000 --cores 4
+
+    $ pygna paint-datasets-stats table_topology_rwr.csv barplot_rwr.pdf
+
+You can look at the plot of the results in the `barplot_rwr.pdf` file, and the corresponding table in  `table_topology_rwr.csv`.
+
+### Example 2: Running pygna GNA analysis
+
+    $ cd ./your-path/min-working-example/
+
+skip this step if the matrix is already computed
+
+    $ pygna build-RWR-diffusion barabasi.interactome.tsv --output-file interactome_RWR.hdf5
+
+The association analysis is run N x M times (N number of genesets, M number of pathways), we use only 50 permutations in this example to avoid long computations; however, the recommended value is 1000.
+
+    $ pygna test-association-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_association_rwr.csv -B disgenet_cancer_groups_subset.gmt --keep --number-of-permutations 100 --cores 4
+
+If you don't include the --results-figure flag at the comparison step, plot the matrix as follows
+
+    $ pygna paint-comparison-matrix table_association_rwr.csv heatmap_association_rwr.png --rwr --annotate
+
+The -k flag, keeps the -B geneset and permutes only on the set A.
+
+If setname B is not passed, the analysis is run between each couple of setnames in the geneset.
+
+    $ pygna test-association-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_within_comparison_rwr.csv --number-of-permutations 100 --cores 4
+
+    $ pygna paint-comparison-matrix table_within_comparison_rwr.csv heatmap_within_comparison_rwr.png --rwr --single-geneset
+
+You can look at the plot of the results in the `heatmap_within_comparison_rwr.png` file, and the corresponding table in  `table_within_comparison_rwr.csv`.
+
+
+## Documentation
+
+The official documentation for `pygna` can be found on [readthedocs](https://pygna.readthedocs.io/).
+
+## Authors
+
+- Viola Fanfani (v.fanfani@sms.ed.ac.uk): lead developer and mantainer.
+- Fabio Cassano (fabio.cassano@ed.ac.uk): support.
+- Giovanni Stracquadanio (giovanni.stracquadanio@ed.ac.uk): corresponding author.
+
+## Citation
+
+V. Fanfani, F. Cassano, and G. Stracquadanio, “PyGNA: a unified framework for geneset network analysis,” BMC Bioinformatics, vol. 21, no. 1, 2020.
+DOI: https://doi.org/10.1186/s12859-020-03801-1
+
+```
+@article{Fanfani2020,
+author = {Fanfani, Viola and Cassano, Fabio and Stracquadanio, Giovanni},
+doi = {10.1186/s12859-020-03801-1},
+issn = {14712105},
+journal = {BMC Bioinformatics},
+number = {1},
+pmid = {33092528},
+title = {{PyGNA: a unified framework for geneset network analysis}},
+volume = {21},
+year = {2020}
+}
+```
+
+## Issues
+
+Please post an issue to report a bug or request new features.
+
+
+
+
+%package -n python3-pygna
+Summary:	Geneset Network Analysis
+Provides:	python-pygna
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pygna
+# PyGNA: a Python framework for geneset network analysis
+
+![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/stracquadaniolab/pygna?style=flat-square)
+[![Anaconda-Server Badge](https://anaconda.org/stracquadaniolab/pygna/badges/version.svg)](https://anaconda.org/stracquadaniolab/pygna)
+![Build](https://github.com/stracquadaniolab/pygna/workflows/Build/badge.svg)
+![Release](https://github.com/stracquadaniolab/pygna/workflows/Release/badge.svg)
+
+PyGNA is a framework for statistical network analysis of high-throughput experiments. It can
+be used both as a standalone command line application or it can be used as API
+to develop custom analyses.
+
+For an overview of PyGNA functionalities check the infographic below or dive into our [Getting started](#getting-started) tour.
+
+![Infographic](docs/pygna_infographic-01.png)
+
+## Installation
+
+The easiest and fastest way to install `pygna` using `conda`:
+
+    $ conda install -c stracquadaniolab -c bioconda -c conda-forge pygna
+
+Alternatively you can install it through `pip`:
+
+    $ pip install pygna
+
+We also provide a docker image installation with the latest version of PyGNA.
+It can be easily executed from the command line from DockerHub:
+
+    $ docker run stracquadaniolab/pygna/pygna:latest
+
+or GitHub Packages:
+
+    $ docker run docker.pkg.github.com/stracquadaniolab/pygna/pygna:latest
+
+
+which will show the PyGNA command line help.
+
+## Getting started
+
+A typical `pygna` analysis consists of 3 steps:
+
+1. Generate the RWR and SP matrices for the network you are using ( once they are generated, you won't need to repeat the same step again)
+2. Make sure that the input genesets are in the right format. If a network uses entrez ID, and your file is in HUGO symbols, use the pygna utility for the name conversion.
+3. Run the analysis you are interested into.
+4. Once you have the output tables, you can choose to visualize one or more plots.
+
+Otherwise you can check our [snakemake workflow](https://github.com/stracquadaniolab/workflow-pygna) for the full geneset analysis;
+our workflow contains sample data that you can use to familiarize with our software.
+
+
+The examples below show some basic analysis that can be carried out with pygna.
+
+### Example 1: Running pygna GNT analysis
+
+Running `pygna` on this input as follows:
+
+    $ cd ./your-path/min-working-example/
+
+    $ pygna build-rwr-diffusion barabasi.interactome.tsv --output-file interactome_RWR.hdf5
+
+    $ pygna test-topology-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_topology_rwr.csv --number-of-permutations 1000 --cores 4
+
+    $ pygna paint-datasets-stats table_topology_rwr.csv barplot_rwr.pdf
+
+You can look at the plot of the results in the `barplot_rwr.pdf` file, and the corresponding table in  `table_topology_rwr.csv`.
+
+### Example 2: Running pygna GNA analysis
+
+    $ cd ./your-path/min-working-example/
+
+skip this step if the matrix is already computed
+
+    $ pygna build-RWR-diffusion barabasi.interactome.tsv --output-file interactome_RWR.hdf5
+
+The association analysis is run N x M times (N number of genesets, M number of pathways), we use only 50 permutations in this example to avoid long computations; however, the recommended value is 1000.
+
+    $ pygna test-association-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_association_rwr.csv -B disgenet_cancer_groups_subset.gmt --keep --number-of-permutations 100 --cores 4
+
+If you don't include the --results-figure flag at the comparison step, plot the matrix as follows
+
+    $ pygna paint-comparison-matrix table_association_rwr.csv heatmap_association_rwr.png --rwr --annotate
+
+The -k flag, keeps the -B geneset and permutes only on the set A.
+
+If setname B is not passed, the analysis is run between each couple of setnames in the geneset.
+
+    $ pygna test-association-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_within_comparison_rwr.csv --number-of-permutations 100 --cores 4
+
+    $ pygna paint-comparison-matrix table_within_comparison_rwr.csv heatmap_within_comparison_rwr.png --rwr --single-geneset
+
+You can look at the plot of the results in the `heatmap_within_comparison_rwr.png` file, and the corresponding table in  `table_within_comparison_rwr.csv`.
+
+
+## Documentation
+
+The official documentation for `pygna` can be found on [readthedocs](https://pygna.readthedocs.io/).
+
+## Authors
+
+- Viola Fanfani (v.fanfani@sms.ed.ac.uk): lead developer and mantainer.
+- Fabio Cassano (fabio.cassano@ed.ac.uk): support.
+- Giovanni Stracquadanio (giovanni.stracquadanio@ed.ac.uk): corresponding author.
+
+## Citation
+
+V. Fanfani, F. Cassano, and G. Stracquadanio, “PyGNA: a unified framework for geneset network analysis,” BMC Bioinformatics, vol. 21, no. 1, 2020.
+DOI: https://doi.org/10.1186/s12859-020-03801-1
+
+```
+@article{Fanfani2020,
+author = {Fanfani, Viola and Cassano, Fabio and Stracquadanio, Giovanni},
+doi = {10.1186/s12859-020-03801-1},
+issn = {14712105},
+journal = {BMC Bioinformatics},
+number = {1},
+pmid = {33092528},
+title = {{PyGNA: a unified framework for geneset network analysis}},
+volume = {21},
+year = {2020}
+}
+```
+
+## Issues
+
+Please post an issue to report a bug or request new features.
+
+
+
+
+%package help
+Summary:	Development documents and examples for pygna
+Provides:	python3-pygna-doc
+%description help
+# PyGNA: a Python framework for geneset network analysis
+
+![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/stracquadaniolab/pygna?style=flat-square)
+[![Anaconda-Server Badge](https://anaconda.org/stracquadaniolab/pygna/badges/version.svg)](https://anaconda.org/stracquadaniolab/pygna)
+![Build](https://github.com/stracquadaniolab/pygna/workflows/Build/badge.svg)
+![Release](https://github.com/stracquadaniolab/pygna/workflows/Release/badge.svg)
+
+PyGNA is a framework for statistical network analysis of high-throughput experiments. It can
+be used both as a standalone command line application or it can be used as API
+to develop custom analyses.
+
+For an overview of PyGNA functionalities check the infographic below or dive into our [Getting started](#getting-started) tour.
+
+![Infographic](docs/pygna_infographic-01.png)
+
+## Installation
+
+The easiest and fastest way to install `pygna` using `conda`:
+
+    $ conda install -c stracquadaniolab -c bioconda -c conda-forge pygna
+
+Alternatively you can install it through `pip`:
+
+    $ pip install pygna
+
+We also provide a docker image installation with the latest version of PyGNA.
+It can be easily executed from the command line from DockerHub:
+
+    $ docker run stracquadaniolab/pygna/pygna:latest
+
+or GitHub Packages:
+
+    $ docker run docker.pkg.github.com/stracquadaniolab/pygna/pygna:latest
+
+
+which will show the PyGNA command line help.
+
+## Getting started
+
+A typical `pygna` analysis consists of 3 steps:
+
+1. Generate the RWR and SP matrices for the network you are using ( once they are generated, you won't need to repeat the same step again)
+2. Make sure that the input genesets are in the right format. If a network uses entrez ID, and your file is in HUGO symbols, use the pygna utility for the name conversion.
+3. Run the analysis you are interested into.
+4. Once you have the output tables, you can choose to visualize one or more plots.
+
+Otherwise you can check our [snakemake workflow](https://github.com/stracquadaniolab/workflow-pygna) for the full geneset analysis;
+our workflow contains sample data that you can use to familiarize with our software.
+
+
+The examples below show some basic analysis that can be carried out with pygna.
+
+### Example 1: Running pygna GNT analysis
+
+Running `pygna` on this input as follows:
+
+    $ cd ./your-path/min-working-example/
+
+    $ pygna build-rwr-diffusion barabasi.interactome.tsv --output-file interactome_RWR.hdf5
+
+    $ pygna test-topology-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_topology_rwr.csv --number-of-permutations 1000 --cores 4
+
+    $ pygna paint-datasets-stats table_topology_rwr.csv barplot_rwr.pdf
+
+You can look at the plot of the results in the `barplot_rwr.pdf` file, and the corresponding table in  `table_topology_rwr.csv`.
+
+### Example 2: Running pygna GNA analysis
+
+    $ cd ./your-path/min-working-example/
+
+skip this step if the matrix is already computed
+
+    $ pygna build-RWR-diffusion barabasi.interactome.tsv --output-file interactome_RWR.hdf5
+
+The association analysis is run N x M times (N number of genesets, M number of pathways), we use only 50 permutations in this example to avoid long computations; however, the recommended value is 1000.
+
+    $ pygna test-association-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_association_rwr.csv -B disgenet_cancer_groups_subset.gmt --keep --number-of-permutations 100 --cores 4
+
+If you don't include the --results-figure flag at the comparison step, plot the matrix as follows
+
+    $ pygna paint-comparison-matrix table_association_rwr.csv heatmap_association_rwr.png --rwr --annotate
+
+The -k flag, keeps the -B geneset and permutes only on the set A.
+
+If setname B is not passed, the analysis is run between each couple of setnames in the geneset.
+
+    $ pygna test-association-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_within_comparison_rwr.csv --number-of-permutations 100 --cores 4
+
+    $ pygna paint-comparison-matrix table_within_comparison_rwr.csv heatmap_within_comparison_rwr.png --rwr --single-geneset
+
+You can look at the plot of the results in the `heatmap_within_comparison_rwr.png` file, and the corresponding table in  `table_within_comparison_rwr.csv`.
+
+
+## Documentation
+
+The official documentation for `pygna` can be found on [readthedocs](https://pygna.readthedocs.io/).
+
+## Authors
+
+- Viola Fanfani (v.fanfani@sms.ed.ac.uk): lead developer and mantainer.
+- Fabio Cassano (fabio.cassano@ed.ac.uk): support.
+- Giovanni Stracquadanio (giovanni.stracquadanio@ed.ac.uk): corresponding author.
+
+## Citation
+
+V. Fanfani, F. Cassano, and G. Stracquadanio, “PyGNA: a unified framework for geneset network analysis,” BMC Bioinformatics, vol. 21, no. 1, 2020.
+DOI: https://doi.org/10.1186/s12859-020-03801-1
+
+```
+@article{Fanfani2020,
+author = {Fanfani, Viola and Cassano, Fabio and Stracquadanio, Giovanni},
+doi = {10.1186/s12859-020-03801-1},
+issn = {14712105},
+journal = {BMC Bioinformatics},
+number = {1},
+pmid = {33092528},
+title = {{PyGNA: a unified framework for geneset network analysis}},
+volume = {21},
+year = {2020}
+}
+```
+
+## Issues
+
+Please post an issue to report a bug or request new features.
+
+
+
+
+%prep
+%autosetup -n pygna-3.3.1
+
+%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-pygna -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 3.3.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..8c75af2
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+0853c47a9a4bbb2fe54e85c54abe47d3  pygna-3.3.1.tar.gz