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| author | CoprDistGit <infra@openeuler.org> | 2023-05-17 05:13:39 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-17 05:13:39 +0000 |
| commit | f39bff425e9343055d5f2d93d6683e94058d5494 (patch) | |
| tree | 81026313993b3a5de8baec192e04cf30803fb52b | |
| parent | fb2145a99d06a445ed6d68ff3447249379beed01 (diff) | |
automatic import of python-acsni
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-acsni.spec | 605 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 607 insertions, 0 deletions
@@ -0,0 +1 @@ +/ACSNI-1.0.6.tar.gz diff --git a/python-acsni.spec b/python-acsni.spec new file mode 100644 index 0000000..73b6ee8 --- /dev/null +++ b/python-acsni.spec @@ -0,0 +1,605 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ACSNI +Version: 1.0.6 +Release: 1 +Summary: automatic context-specific network inference +License: MIT License +URL: https://github.com/caanene1/ACSNI +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/74/a0/2105902b302f3845e34989475fd5fd66dcad2a3852320cce1d8db9f0cfd1/ACSNI-1.0.6.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-scikit-learn +Requires: python3-scipy +Requires: python3-tensorflow + +%description +# ACSNI +Automatic context-specific network inference + +Determining tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. +Many components of these biological pathways still remain unknown, hindering the full and accurate characterisation of +biological processes of interest. ACSNI leverages artificial intelligence for the reconstruction of a biological pathway, +aids the discovery of pathway components and classification of the crosstalk between pathways in specific tissues. + + + +This tool is built in python3.8 with tensorflow backend and keras functional API. + +# Installation and running the tool +The best way to get ACSNI along with all the dependencies is to install the release from python package installer (pip) + +```pip install ACSNI``` +This will add four command line scripts: + +| Script | Context | Usage | +| --- | --- | --- | +| ACSNI-run | Gene set analysis | ```ACSNI-run -h``` | +| ACSNI-derive | Single gene analysis | ```ACSNI-derive -h``` | +| ACSNI-get | Link pathway trait | ```ACSNI-get -h``` | +| ACSNI-split | Split expression data | ```ACSNI-split -h``` | + +Utility functions can be imported using conventional python system like ```from ACSNI.dbs import ACSNIResults``` + + +# Input ACSNI-run +Expression Matrix - The expression file (.csv), specified by ```-i```, where columns are samples and rows are genes. +The expression values should be normalised (eg. TPM, CPM, RSEM). Make sure the column name of the 1st column is "gene". + +| gene | Sample1 | Sample2 | Sample3 | +| --- | --- | --- | --- | +| Foxp1 | 123.2 | 274.1 | 852.6 | +| PD1 | 324.2 | 494.1 | 452.6 | +| CD8 | 523.6 | 624.1 | 252.6 | + +This input should not be transformed in any way (e.g. log, z-scale) + +Gene set matrix - The prior matrix (.csv) file, specified by ```-t```, where rows are genes and column is a binary +pathway membership. Where "1" means that a gene is in the pathway and "0" means that the gene is not know a priori. +The standard prior looks like below. Make sure the column name of the 1st column is "gene". + +| gene | Pathway | +| --- | --- | +| Foxp1 | 0 | +| PD1 | 0 | +| CD8 | 1 | + +You can also supply gene IDs instead of gene symbols. + +The tool can handle multiple pathway columns in the ```-t``` file as below. + +| gene | Pathway1 | Pathway2 | Pathway3 | +| --- | --- | --- | --- | +| Foxp1 | 0 | 0 | 0 | +| PD1 | 0 | 1 | 0 | +| CD8 | 1 | 0 | 1 | + +Note: Each pathway above is analysed independently, and the outputs have no in-built relationship. +The tool is designed to get a granular view of a single pathway at a time. + +# Output ACSNI-run +Database (.ptl) + +| Content | Information | +| --- | --- | +| co | Pathway Code| +| w | Subprocess space | +| n | Interaction scores | +| p | Score classification | +| d | Interaction direction | +| run_info | Run parameters | +| methods | Extractor functions | + +Predicted Network (.csv) + +| Content | Meaning | +| --- | --- | +| name | Gene | +| sub | Subprocess | +| direction | Direction of interactions with subprocess | + +Null (.csv) {Shuffled expression matrix} + + + +# Input ACSNI-derive + +Expression Matrix - See ``-i``` description above. + +Note - We recommend removing any un-desirable genes (eg. MT, RPL) from the expression +matrix prior to running ACSNI-derive as they usually interfere during initial prior matrix generation steps. +For TCR/BCR genes, counts of alpha, beta and gamma chains can be combined into a single count. + +Biotype file (Optional) - The biotype file (.csv) specified by ```-f```, given if the generation of gene set should be +based on a particular biotype specified by ```-b```. + +| gene | biotype | +| --- | --- | +| Foxp1 | protein_coding | +| PD1 | protein_coding | +| MALAT1 | lncRNA | +| SNHG12 | lncRNA | +| RNU1-114P | snRNA | + +Correlation file (Optional) - The correlation file (.csv) specified by ```-u```, given if the user wishes to replace +"some" specific genes with other genes to be used as a prior for the first iteration of ACSNI-run (internally). + +| gene | cor | +| --- | --- | +| Foxp1 | 0.9 | +| PD1 | 0.89 | +| MALAT1 | 0.85 | +| SNHG12 | 0.80 | +| RNU1-114P | 0.72 | + +# Output ACSNI-derive +Database (.ptl) + +| Content | Information | +| --- | --- | +| co | Pathway Code| +| n | Interaction scores | +| d | Interaction direction | +| ac | Correlation and T test results | +| fd | Unfiltered prediction data | +| run_info | Run parameters | +| methods | Extractor functions | + +Predicted (.csv) + +| Content | Meaning | +| --- | --- | +| name | Gene | +| predict | Classification of genes| + +Null (.csv) {Shuffled expression matrix} + + +# Input ACSNI-get + +ACSNI database - Output of ACSNI-run (.ptl) specified by ```-r```. + +Target phenotype - Biological phenotype file (.csv) to link ACSNI subprocesses, specified by ```-v```. +The sample IDs should match the IDs in the ```-i``` analysed by ACSNI-run. + +Variable type - The type of phenotype i.e "numeric" or "character", specified by ```-c```. + +Outputs the strength of the associations across the subprocesses (.csv). + +# Input ACSNI-split + +Expression Matrix - See ``-i``` description above. + +Number of splits - The number of independent cohorts to generate from `-i```. + +Outputs the data splits in the current working directory. + +# Extras +R functions to reproduce the downstream analyses reported in the paper are inside the folder "R". + +Example runs are inside the folder "sh". + +# Tutorial +An extensive tutorial on how to use ACSNI commands can be found inside the Tutorial folder. + +# To clone the source repository +git clone https://github.com/caanene1/ACSNI + +# Citation +ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles +Chinedu Anthony Anene, Faraz Khan, Findlay Bewicke-Copley, Eleni Maniati and Jun Wang + + + + +%package -n python3-ACSNI +Summary: automatic context-specific network inference +Provides: python-ACSNI +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-ACSNI +# ACSNI +Automatic context-specific network inference + +Determining tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. +Many components of these biological pathways still remain unknown, hindering the full and accurate characterisation of +biological processes of interest. ACSNI leverages artificial intelligence for the reconstruction of a biological pathway, +aids the discovery of pathway components and classification of the crosstalk between pathways in specific tissues. + + + +This tool is built in python3.8 with tensorflow backend and keras functional API. + +# Installation and running the tool +The best way to get ACSNI along with all the dependencies is to install the release from python package installer (pip) + +```pip install ACSNI``` +This will add four command line scripts: + +| Script | Context | Usage | +| --- | --- | --- | +| ACSNI-run | Gene set analysis | ```ACSNI-run -h``` | +| ACSNI-derive | Single gene analysis | ```ACSNI-derive -h``` | +| ACSNI-get | Link pathway trait | ```ACSNI-get -h``` | +| ACSNI-split | Split expression data | ```ACSNI-split -h``` | + +Utility functions can be imported using conventional python system like ```from ACSNI.dbs import ACSNIResults``` + + +# Input ACSNI-run +Expression Matrix - The expression file (.csv), specified by ```-i```, where columns are samples and rows are genes. +The expression values should be normalised (eg. TPM, CPM, RSEM). Make sure the column name of the 1st column is "gene". + +| gene | Sample1 | Sample2 | Sample3 | +| --- | --- | --- | --- | +| Foxp1 | 123.2 | 274.1 | 852.6 | +| PD1 | 324.2 | 494.1 | 452.6 | +| CD8 | 523.6 | 624.1 | 252.6 | + +This input should not be transformed in any way (e.g. log, z-scale) + +Gene set matrix - The prior matrix (.csv) file, specified by ```-t```, where rows are genes and column is a binary +pathway membership. Where "1" means that a gene is in the pathway and "0" means that the gene is not know a priori. +The standard prior looks like below. Make sure the column name of the 1st column is "gene". + +| gene | Pathway | +| --- | --- | +| Foxp1 | 0 | +| PD1 | 0 | +| CD8 | 1 | + +You can also supply gene IDs instead of gene symbols. + +The tool can handle multiple pathway columns in the ```-t``` file as below. + +| gene | Pathway1 | Pathway2 | Pathway3 | +| --- | --- | --- | --- | +| Foxp1 | 0 | 0 | 0 | +| PD1 | 0 | 1 | 0 | +| CD8 | 1 | 0 | 1 | + +Note: Each pathway above is analysed independently, and the outputs have no in-built relationship. +The tool is designed to get a granular view of a single pathway at a time. + +# Output ACSNI-run +Database (.ptl) + +| Content | Information | +| --- | --- | +| co | Pathway Code| +| w | Subprocess space | +| n | Interaction scores | +| p | Score classification | +| d | Interaction direction | +| run_info | Run parameters | +| methods | Extractor functions | + +Predicted Network (.csv) + +| Content | Meaning | +| --- | --- | +| name | Gene | +| sub | Subprocess | +| direction | Direction of interactions with subprocess | + +Null (.csv) {Shuffled expression matrix} + + + +# Input ACSNI-derive + +Expression Matrix - See ``-i``` description above. + +Note - We recommend removing any un-desirable genes (eg. MT, RPL) from the expression +matrix prior to running ACSNI-derive as they usually interfere during initial prior matrix generation steps. +For TCR/BCR genes, counts of alpha, beta and gamma chains can be combined into a single count. + +Biotype file (Optional) - The biotype file (.csv) specified by ```-f```, given if the generation of gene set should be +based on a particular biotype specified by ```-b```. + +| gene | biotype | +| --- | --- | +| Foxp1 | protein_coding | +| PD1 | protein_coding | +| MALAT1 | lncRNA | +| SNHG12 | lncRNA | +| RNU1-114P | snRNA | + +Correlation file (Optional) - The correlation file (.csv) specified by ```-u```, given if the user wishes to replace +"some" specific genes with other genes to be used as a prior for the first iteration of ACSNI-run (internally). + +| gene | cor | +| --- | --- | +| Foxp1 | 0.9 | +| PD1 | 0.89 | +| MALAT1 | 0.85 | +| SNHG12 | 0.80 | +| RNU1-114P | 0.72 | + +# Output ACSNI-derive +Database (.ptl) + +| Content | Information | +| --- | --- | +| co | Pathway Code| +| n | Interaction scores | +| d | Interaction direction | +| ac | Correlation and T test results | +| fd | Unfiltered prediction data | +| run_info | Run parameters | +| methods | Extractor functions | + +Predicted (.csv) + +| Content | Meaning | +| --- | --- | +| name | Gene | +| predict | Classification of genes| + +Null (.csv) {Shuffled expression matrix} + + +# Input ACSNI-get + +ACSNI database - Output of ACSNI-run (.ptl) specified by ```-r```. + +Target phenotype - Biological phenotype file (.csv) to link ACSNI subprocesses, specified by ```-v```. +The sample IDs should match the IDs in the ```-i``` analysed by ACSNI-run. + +Variable type - The type of phenotype i.e "numeric" or "character", specified by ```-c```. + +Outputs the strength of the associations across the subprocesses (.csv). + +# Input ACSNI-split + +Expression Matrix - See ``-i``` description above. + +Number of splits - The number of independent cohorts to generate from `-i```. + +Outputs the data splits in the current working directory. + +# Extras +R functions to reproduce the downstream analyses reported in the paper are inside the folder "R". + +Example runs are inside the folder "sh". + +# Tutorial +An extensive tutorial on how to use ACSNI commands can be found inside the Tutorial folder. + +# To clone the source repository +git clone https://github.com/caanene1/ACSNI + +# Citation +ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles +Chinedu Anthony Anene, Faraz Khan, Findlay Bewicke-Copley, Eleni Maniati and Jun Wang + + + + +%package help +Summary: Development documents and examples for ACSNI +Provides: python3-ACSNI-doc +%description help +# ACSNI +Automatic context-specific network inference + +Determining tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. +Many components of these biological pathways still remain unknown, hindering the full and accurate characterisation of +biological processes of interest. ACSNI leverages artificial intelligence for the reconstruction of a biological pathway, +aids the discovery of pathway components and classification of the crosstalk between pathways in specific tissues. + + + +This tool is built in python3.8 with tensorflow backend and keras functional API. + +# Installation and running the tool +The best way to get ACSNI along with all the dependencies is to install the release from python package installer (pip) + +```pip install ACSNI``` +This will add four command line scripts: + +| Script | Context | Usage | +| --- | --- | --- | +| ACSNI-run | Gene set analysis | ```ACSNI-run -h``` | +| ACSNI-derive | Single gene analysis | ```ACSNI-derive -h``` | +| ACSNI-get | Link pathway trait | ```ACSNI-get -h``` | +| ACSNI-split | Split expression data | ```ACSNI-split -h``` | + +Utility functions can be imported using conventional python system like ```from ACSNI.dbs import ACSNIResults``` + + +# Input ACSNI-run +Expression Matrix - The expression file (.csv), specified by ```-i```, where columns are samples and rows are genes. +The expression values should be normalised (eg. TPM, CPM, RSEM). Make sure the column name of the 1st column is "gene". + +| gene | Sample1 | Sample2 | Sample3 | +| --- | --- | --- | --- | +| Foxp1 | 123.2 | 274.1 | 852.6 | +| PD1 | 324.2 | 494.1 | 452.6 | +| CD8 | 523.6 | 624.1 | 252.6 | + +This input should not be transformed in any way (e.g. log, z-scale) + +Gene set matrix - The prior matrix (.csv) file, specified by ```-t```, where rows are genes and column is a binary +pathway membership. Where "1" means that a gene is in the pathway and "0" means that the gene is not know a priori. +The standard prior looks like below. Make sure the column name of the 1st column is "gene". + +| gene | Pathway | +| --- | --- | +| Foxp1 | 0 | +| PD1 | 0 | +| CD8 | 1 | + +You can also supply gene IDs instead of gene symbols. + +The tool can handle multiple pathway columns in the ```-t``` file as below. + +| gene | Pathway1 | Pathway2 | Pathway3 | +| --- | --- | --- | --- | +| Foxp1 | 0 | 0 | 0 | +| PD1 | 0 | 1 | 0 | +| CD8 | 1 | 0 | 1 | + +Note: Each pathway above is analysed independently, and the outputs have no in-built relationship. +The tool is designed to get a granular view of a single pathway at a time. + +# Output ACSNI-run +Database (.ptl) + +| Content | Information | +| --- | --- | +| co | Pathway Code| +| w | Subprocess space | +| n | Interaction scores | +| p | Score classification | +| d | Interaction direction | +| run_info | Run parameters | +| methods | Extractor functions | + +Predicted Network (.csv) + +| Content | Meaning | +| --- | --- | +| name | Gene | +| sub | Subprocess | +| direction | Direction of interactions with subprocess | + +Null (.csv) {Shuffled expression matrix} + + + +# Input ACSNI-derive + +Expression Matrix - See ``-i``` description above. + +Note - We recommend removing any un-desirable genes (eg. MT, RPL) from the expression +matrix prior to running ACSNI-derive as they usually interfere during initial prior matrix generation steps. +For TCR/BCR genes, counts of alpha, beta and gamma chains can be combined into a single count. + +Biotype file (Optional) - The biotype file (.csv) specified by ```-f```, given if the generation of gene set should be +based on a particular biotype specified by ```-b```. + +| gene | biotype | +| --- | --- | +| Foxp1 | protein_coding | +| PD1 | protein_coding | +| MALAT1 | lncRNA | +| SNHG12 | lncRNA | +| RNU1-114P | snRNA | + +Correlation file (Optional) - The correlation file (.csv) specified by ```-u```, given if the user wishes to replace +"some" specific genes with other genes to be used as a prior for the first iteration of ACSNI-run (internally). + +| gene | cor | +| --- | --- | +| Foxp1 | 0.9 | +| PD1 | 0.89 | +| MALAT1 | 0.85 | +| SNHG12 | 0.80 | +| RNU1-114P | 0.72 | + +# Output ACSNI-derive +Database (.ptl) + +| Content | Information | +| --- | --- | +| co | Pathway Code| +| n | Interaction scores | +| d | Interaction direction | +| ac | Correlation and T test results | +| fd | Unfiltered prediction data | +| run_info | Run parameters | +| methods | Extractor functions | + +Predicted (.csv) + +| Content | Meaning | +| --- | --- | +| name | Gene | +| predict | Classification of genes| + +Null (.csv) {Shuffled expression matrix} + + +# Input ACSNI-get + +ACSNI database - Output of ACSNI-run (.ptl) specified by ```-r```. + +Target phenotype - Biological phenotype file (.csv) to link ACSNI subprocesses, specified by ```-v```. +The sample IDs should match the IDs in the ```-i``` analysed by ACSNI-run. + +Variable type - The type of phenotype i.e "numeric" or "character", specified by ```-c```. + +Outputs the strength of the associations across the subprocesses (.csv). + +# Input ACSNI-split + +Expression Matrix - See ``-i``` description above. + +Number of splits - The number of independent cohorts to generate from `-i```. + +Outputs the data splits in the current working directory. + +# Extras +R functions to reproduce the downstream analyses reported in the paper are inside the folder "R". + +Example runs are inside the folder "sh". + +# Tutorial +An extensive tutorial on how to use ACSNI commands can be found inside the Tutorial folder. + +# To clone the source repository +git clone https://github.com/caanene1/ACSNI + +# Citation +ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles +Chinedu Anthony Anene, Faraz Khan, Findlay Bewicke-Copley, Eleni Maniati and Jun Wang + + + + +%prep +%autosetup -n ACSNI-1.0.6 + +%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-ACSNI -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.6-1 +- Package Spec generated @@ -0,0 +1 @@ +5a9253cacccb003eb282829fe49e16ce ACSNI-1.0.6.tar.gz |