automatic import of python-biolearns

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+/biolearns-0.0.62.tar.gz
diff --git a/python-biolearns.spec b/python-biolearns.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-biolearns
+Version:	0.0.62
+Release:	1
+Summary:	BioLearns: Computational Biology and Bioinformatics Toolbox in Python
+License:	MIT License
+URL:		https://pypi.org/project/biolearns/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/e2/22/55d18231c9d8cf773852fd9f52167bbdaa015f479ec33508a664a1fa9c51/biolearns-0.0.62.tar.gz
+BuildArch:	noarch
+
+
+%description
+# biolearns
+BioLearns: Computational Biology and Bioinformatics Toolbox in Python http://biolearns.medicine.iu.edu
+
+<div style="text-align:center"><img src="figures/logo.png" width=300/></div>
+
+[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/huangzhii/biolearns/blob/master/LICENSE)
+
+## Installation
+
+* From PyPI
+
+```bash
+pip install biolearns -U
+```
+
+## Documentation and Tutorials
+
+* We select three examples listed below. For full list of tutorial, check our github wiki page:
+
+    [Wiki](https://github.com/huangzhii/biolearns/wiki)
+
+## Disclaimer
+Please note that this is a pre-release version of the BioLearns which is still undergoing final testing before its official release. The website, its software and all content found on it are provided on an "as is" and "as available" basis. BioLearns does not give any warranties, whether express or implied, as to the suitability or usability of the website, its software or any of its content. BioLearns will not be liable for any loss, whether such loss is direct, indirect, special or consequential, suffered by any party as a result of their use of the libraries or content. Any usage of the libraries is done at the user's own risk and the user will be solely responsible for any damage to any computer system or loss of data that results from such activities. Should you encounter any bugs, glitches, lack of functionality or other problems on the website, please let us know immediately so we can rectify these accordingly. Your help in this regard is greatly appreciated.
+
+
+
+### 1. Read TCGA Data
+
+#### Example: Read TCGA Breast invasive carcinoma (BRCA) data
+
+Data is downloaded directly from https://gdac.broadinstitute.org/.
+The results here are in whole or part based upon data generated by 
+the TCGA Research Network: https://www.cancer.gov/tcga.
+
+```python
+from biolearns.dataset import TCGA
+```
+
+```python
+brca = TCGA('BRCA')
+mRNAseq = brca.mRNAseq
+clinical = brca.clinical
+```
+
+#### TCGA cancer table shortcut:
+
+|              | Barcode            | Cancer full name         | Version            |
+|---|---|---|---|
+| 1      |  ACC          |  Adrenocortical carcinoma     | 2016_01_28 |
+| 2      |  BLCA         |  Bladder urothelial carcinoma         | 2016_01_28 |
+| 3      |  BRCA         |  Breast invasive carcinoma    | 2016_01_28 |
+| 4      |  CESC         |  Cervical and endocervical cancers    | 2016_01_28 |
+| 5      |  CHOL         |  Cholangiocarcinoma   | 2016_01_28 |
+| 6      |  COAD         |  Colon adenocarcinoma         | 2016_01_28 |
+| 7      |  COADREAD     |  Colorectal adenocarcinoma    | 2016_01_28 |
+| 8      |  DLBC         |  Lymphoid Neoplasm Diffuse Large B-cell Lymphoma      | 2016_01_28 |
+| 9      |  ESCA         |  Esophageal carcinoma         | 2016_01_28 |
+| ...     |  ...         |  ...          | ... |
+
+
+### 2. Gene Co-expression Analysis
+
+We firstly download and access the mRNAseq data.
+```python
+from biolearns.dataset import TCGA
+
+brca = TCGA('BRCA')
+mRNAseq = brca.mRNAseq
+```
+
+mRNAseq data is noisy. We filter out 50% of genes with lowest mean values, and then filter out 50% remained genes with lowest variance values.
+
+```python
+from biolearns.preprocessing import expression_filter
+mRNAseq = expression_filter(mRNAseq, meanq = 0.5, varq = 0.5)
+```
+
+We then use lmQCM class to create an lmQCM object ```lobj```.
+
+The gene co-expression analysis is performed by simply call the ```fit()``` function.
+
+```python
+from biolearns.coexpression import lmQCM
+
+lobj = lmQCM(mRNAseq)
+clusters, genes, eigengene_mat = lobj.fit()
+```
+
+### 3. Univariate survival analysis
+
+We firstly download and access the mRNAseq data. Use breast cancer as an example.
+```python
+from biolearns.dataset import TCGA
+
+brca = TCGA('BRCA')
+mRNAseq = brca.mRNAseq
+```
+
+We import logranktest from survival subpackage. Choose gene "ABLIM3" as the univariate input.
+```python
+from biolearns.survival import logranktest
+
+r = mRNAseq.loc['ABLIM3',].values
+```
+
+We find the intersection of univariate, time, and event data
+```python
+bcd_m = [b[:12] for b in mRNAseq.columns]
+bcd_p = [b[:12] for b in clinical.index]
+bcd = np.intersect1d(bcd_m, bcd_p)
+
+r = r[np.nonzero(np.in1d(bcd, bcd_m))[0]]
+t = brca.overall_survival_time[np.nonzero(np.in1d(bcd, bcd_p))[0]]
+e = brca.overall_survival_event[np.nonzero(np.in1d(bcd, bcd_p))[0]]
+```
+
+We perform log-rank test:
+
+```python
+logrank_results, fig = logranktest(r[~np.isnan(t)], t[~np.isnan(t)], e[~np.isnan(t)])
+test_statistic, p_value = logrank_results.test_statistic, logrank_results.p_value
+```
+
+The output figure looks like:
+
+<div style="text-align:center"><img src="https://github.com/huangzhii/biolearns/blob/master/figures/survival_plot_BRCA_ABLIM3.png" width=600/></div>
+
+
+%package -n python3-biolearns
+Summary:	BioLearns: Computational Biology and Bioinformatics Toolbox in Python
+Provides:	python-biolearns
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-biolearns
+# biolearns
+BioLearns: Computational Biology and Bioinformatics Toolbox in Python http://biolearns.medicine.iu.edu
+
+<div style="text-align:center"><img src="figures/logo.png" width=300/></div>
+
+[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/huangzhii/biolearns/blob/master/LICENSE)
+
+## Installation
+
+* From PyPI
+
+```bash
+pip install biolearns -U
+```
+
+## Documentation and Tutorials
+
+* We select three examples listed below. For full list of tutorial, check our github wiki page:
+
+    [Wiki](https://github.com/huangzhii/biolearns/wiki)
+
+## Disclaimer
+Please note that this is a pre-release version of the BioLearns which is still undergoing final testing before its official release. The website, its software and all content found on it are provided on an "as is" and "as available" basis. BioLearns does not give any warranties, whether express or implied, as to the suitability or usability of the website, its software or any of its content. BioLearns will not be liable for any loss, whether such loss is direct, indirect, special or consequential, suffered by any party as a result of their use of the libraries or content. Any usage of the libraries is done at the user's own risk and the user will be solely responsible for any damage to any computer system or loss of data that results from such activities. Should you encounter any bugs, glitches, lack of functionality or other problems on the website, please let us know immediately so we can rectify these accordingly. Your help in this regard is greatly appreciated.
+
+
+
+### 1. Read TCGA Data
+
+#### Example: Read TCGA Breast invasive carcinoma (BRCA) data
+
+Data is downloaded directly from https://gdac.broadinstitute.org/.
+The results here are in whole or part based upon data generated by 
+the TCGA Research Network: https://www.cancer.gov/tcga.
+
+```python
+from biolearns.dataset import TCGA
+```
+
+```python
+brca = TCGA('BRCA')
+mRNAseq = brca.mRNAseq
+clinical = brca.clinical
+```
+
+#### TCGA cancer table shortcut:
+
+|              | Barcode            | Cancer full name         | Version            |
+|---|---|---|---|
+| 1      |  ACC          |  Adrenocortical carcinoma     | 2016_01_28 |
+| 2      |  BLCA         |  Bladder urothelial carcinoma         | 2016_01_28 |
+| 3      |  BRCA         |  Breast invasive carcinoma    | 2016_01_28 |
+| 4      |  CESC         |  Cervical and endocervical cancers    | 2016_01_28 |
+| 5      |  CHOL         |  Cholangiocarcinoma   | 2016_01_28 |
+| 6      |  COAD         |  Colon adenocarcinoma         | 2016_01_28 |
+| 7      |  COADREAD     |  Colorectal adenocarcinoma    | 2016_01_28 |
+| 8      |  DLBC         |  Lymphoid Neoplasm Diffuse Large B-cell Lymphoma      | 2016_01_28 |
+| 9      |  ESCA         |  Esophageal carcinoma         | 2016_01_28 |
+| ...     |  ...         |  ...          | ... |
+
+
+### 2. Gene Co-expression Analysis
+
+We firstly download and access the mRNAseq data.
+```python
+from biolearns.dataset import TCGA
+
+brca = TCGA('BRCA')
+mRNAseq = brca.mRNAseq
+```
+
+mRNAseq data is noisy. We filter out 50% of genes with lowest mean values, and then filter out 50% remained genes with lowest variance values.
+
+```python
+from biolearns.preprocessing import expression_filter
+mRNAseq = expression_filter(mRNAseq, meanq = 0.5, varq = 0.5)
+```
+
+We then use lmQCM class to create an lmQCM object ```lobj```.
+
+The gene co-expression analysis is performed by simply call the ```fit()``` function.
+
+```python
+from biolearns.coexpression import lmQCM
+
+lobj = lmQCM(mRNAseq)
+clusters, genes, eigengene_mat = lobj.fit()
+```
+
+### 3. Univariate survival analysis
+
+We firstly download and access the mRNAseq data. Use breast cancer as an example.
+```python
+from biolearns.dataset import TCGA
+
+brca = TCGA('BRCA')
+mRNAseq = brca.mRNAseq
+```
+
+We import logranktest from survival subpackage. Choose gene "ABLIM3" as the univariate input.
+```python
+from biolearns.survival import logranktest
+
+r = mRNAseq.loc['ABLIM3',].values
+```
+
+We find the intersection of univariate, time, and event data
+```python
+bcd_m = [b[:12] for b in mRNAseq.columns]
+bcd_p = [b[:12] for b in clinical.index]
+bcd = np.intersect1d(bcd_m, bcd_p)
+
+r = r[np.nonzero(np.in1d(bcd, bcd_m))[0]]
+t = brca.overall_survival_time[np.nonzero(np.in1d(bcd, bcd_p))[0]]
+e = brca.overall_survival_event[np.nonzero(np.in1d(bcd, bcd_p))[0]]
+```
+
+We perform log-rank test:
+
+```python
+logrank_results, fig = logranktest(r[~np.isnan(t)], t[~np.isnan(t)], e[~np.isnan(t)])
+test_statistic, p_value = logrank_results.test_statistic, logrank_results.p_value
+```
+
+The output figure looks like:
+
+<div style="text-align:center"><img src="https://github.com/huangzhii/biolearns/blob/master/figures/survival_plot_BRCA_ABLIM3.png" width=600/></div>
+
+
+%package help
+Summary:	Development documents and examples for biolearns
+Provides:	python3-biolearns-doc
+%description help
+# biolearns
+BioLearns: Computational Biology and Bioinformatics Toolbox in Python http://biolearns.medicine.iu.edu
+
+<div style="text-align:center"><img src="figures/logo.png" width=300/></div>
+
+[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/huangzhii/biolearns/blob/master/LICENSE)
+
+## Installation
+
+* From PyPI
+
+```bash
+pip install biolearns -U
+```
+
+## Documentation and Tutorials
+
+* We select three examples listed below. For full list of tutorial, check our github wiki page:
+
+    [Wiki](https://github.com/huangzhii/biolearns/wiki)
+
+## Disclaimer
+Please note that this is a pre-release version of the BioLearns which is still undergoing final testing before its official release. The website, its software and all content found on it are provided on an "as is" and "as available" basis. BioLearns does not give any warranties, whether express or implied, as to the suitability or usability of the website, its software or any of its content. BioLearns will not be liable for any loss, whether such loss is direct, indirect, special or consequential, suffered by any party as a result of their use of the libraries or content. Any usage of the libraries is done at the user's own risk and the user will be solely responsible for any damage to any computer system or loss of data that results from such activities. Should you encounter any bugs, glitches, lack of functionality or other problems on the website, please let us know immediately so we can rectify these accordingly. Your help in this regard is greatly appreciated.
+
+
+
+### 1. Read TCGA Data
+
+#### Example: Read TCGA Breast invasive carcinoma (BRCA) data
+
+Data is downloaded directly from https://gdac.broadinstitute.org/.
+The results here are in whole or part based upon data generated by 
+the TCGA Research Network: https://www.cancer.gov/tcga.
+
+```python
+from biolearns.dataset import TCGA
+```
+
+```python
+brca = TCGA('BRCA')
+mRNAseq = brca.mRNAseq
+clinical = brca.clinical
+```
+
+#### TCGA cancer table shortcut:
+
+|              | Barcode            | Cancer full name         | Version            |
+|---|---|---|---|
+| 1      |  ACC          |  Adrenocortical carcinoma     | 2016_01_28 |
+| 2      |  BLCA         |  Bladder urothelial carcinoma         | 2016_01_28 |
+| 3      |  BRCA         |  Breast invasive carcinoma    | 2016_01_28 |
+| 4      |  CESC         |  Cervical and endocervical cancers    | 2016_01_28 |
+| 5      |  CHOL         |  Cholangiocarcinoma   | 2016_01_28 |
+| 6      |  COAD         |  Colon adenocarcinoma         | 2016_01_28 |
+| 7      |  COADREAD     |  Colorectal adenocarcinoma    | 2016_01_28 |
+| 8      |  DLBC         |  Lymphoid Neoplasm Diffuse Large B-cell Lymphoma      | 2016_01_28 |
+| 9      |  ESCA         |  Esophageal carcinoma         | 2016_01_28 |
+| ...     |  ...         |  ...          | ... |
+
+
+### 2. Gene Co-expression Analysis
+
+We firstly download and access the mRNAseq data.
+```python
+from biolearns.dataset import TCGA
+
+brca = TCGA('BRCA')
+mRNAseq = brca.mRNAseq
+```
+
+mRNAseq data is noisy. We filter out 50% of genes with lowest mean values, and then filter out 50% remained genes with lowest variance values.
+
+```python
+from biolearns.preprocessing import expression_filter
+mRNAseq = expression_filter(mRNAseq, meanq = 0.5, varq = 0.5)
+```
+
+We then use lmQCM class to create an lmQCM object ```lobj```.
+
+The gene co-expression analysis is performed by simply call the ```fit()``` function.
+
+```python
+from biolearns.coexpression import lmQCM
+
+lobj = lmQCM(mRNAseq)
+clusters, genes, eigengene_mat = lobj.fit()
+```
+
+### 3. Univariate survival analysis
+
+We firstly download and access the mRNAseq data. Use breast cancer as an example.
+```python
+from biolearns.dataset import TCGA
+
+brca = TCGA('BRCA')
+mRNAseq = brca.mRNAseq
+```
+
+We import logranktest from survival subpackage. Choose gene "ABLIM3" as the univariate input.
+```python
+from biolearns.survival import logranktest
+
+r = mRNAseq.loc['ABLIM3',].values
+```
+
+We find the intersection of univariate, time, and event data
+```python
+bcd_m = [b[:12] for b in mRNAseq.columns]
+bcd_p = [b[:12] for b in clinical.index]
+bcd = np.intersect1d(bcd_m, bcd_p)
+
+r = r[np.nonzero(np.in1d(bcd, bcd_m))[0]]
+t = brca.overall_survival_time[np.nonzero(np.in1d(bcd, bcd_p))[0]]
+e = brca.overall_survival_event[np.nonzero(np.in1d(bcd, bcd_p))[0]]
+```
+
+We perform log-rank test:
+
+```python
+logrank_results, fig = logranktest(r[~np.isnan(t)], t[~np.isnan(t)], e[~np.isnan(t)])
+test_statistic, p_value = logrank_results.test_statistic, logrank_results.p_value
+```
+
+The output figure looks like:
+
+<div style="text-align:center"><img src="https://github.com/huangzhii/biolearns/blob/master/figures/survival_plot_BRCA_ABLIM3.png" width=600/></div>
+
+
+%prep
+%autosetup -n biolearns-0.0.62
+
+%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-biolearns -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.62-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..4a73593
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+01d0d0c5a7931f859c49f60621377375  biolearns-0.0.62.tar.gz