%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.aliyun.com/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
[![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:
%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
[![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:
%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
[![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:
%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 * Thu Jun 08 2023 Python_Bot - 0.0.62-1 - Package Spec generated