%global _empty_manifest_terminate_build 0 Name: python-congas Version: 0.0.75 Release: 1 Summary: Copy Number genotyping from single cell RNA sequencing License: GPL-3.0 URL: https://github.com/Militeee/congas Source0: https://mirrors.aliyun.com/pypi/web/packages/0b/81/40cafaca7406d60c112f93d1ea0bc0208cfa06667f5b1ef0510bd26eb7fc/congas-0.0.75.tar.gz BuildArch: noarch Requires: python3-matplotlib Requires: python3-pandas Requires: python3-pyro-ppl Requires: python3-numpy Requires: python3-scikit-learn %description # Copy number genotyping from scRNA sequencing [![Build Status](https://travis-ci.org/Militeee/anneal.svg?branch=master)](https://travis-ci.org/Militeee/congas) [![codecov](https://codecov.io/gh/Militeee/anneal/branch/master/graph/badge.svg)](https://codecov.io/gh/Militeee/congas) A set of Pyro models and functions to infer CNA from scRNA-seq data. It comes with a companion R package (hlink) that works as an interface and provides preprocessing, simulation and visualization routines. Currently providing: - A mixture model on segments where CNV are modelled as LogNormal random variable (MixtureGaussian) - Same as above but the number of cluster is learned (MixtureGaussianDMP) - A model where CNVs are modelled as outcome from Categorical distributions, clusters share the same parameters (MixtureDirichlet) - A simple Hmm where CNVs are again categorical, but there is no clustering (SimpleHmm) - The version of MixtureDirichlet but with temporal dependency (HmmMixtureRNA) Coming soon: - A linear model in the emission that can account for known covariates - The equivalent of MixtureGaussian but with CNVs as Categorical random variable - A model on genes (all the other models assume a division in segments) To install: `$ pip install congas` To run a simple analysis on the example data ```python import congas as cn from congas.models import MixtureGaussian data_dict = cn.simulation_data params, loss = cn.run_analysis(data_dict,MixtureGaussian, steps=200, lr=0.05) ``` [Full Documentation](https://annealpyro.readthedocs.io/en/latest/) %package -n python3-congas Summary: Copy Number genotyping from single cell RNA sequencing Provides: python-congas BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-congas # Copy number genotyping from scRNA sequencing [![Build Status](https://travis-ci.org/Militeee/anneal.svg?branch=master)](https://travis-ci.org/Militeee/congas) [![codecov](https://codecov.io/gh/Militeee/anneal/branch/master/graph/badge.svg)](https://codecov.io/gh/Militeee/congas) A set of Pyro models and functions to infer CNA from scRNA-seq data. It comes with a companion R package (hlink) that works as an interface and provides preprocessing, simulation and visualization routines. Currently providing: - A mixture model on segments where CNV are modelled as LogNormal random variable (MixtureGaussian) - Same as above but the number of cluster is learned (MixtureGaussianDMP) - A model where CNVs are modelled as outcome from Categorical distributions, clusters share the same parameters (MixtureDirichlet) - A simple Hmm where CNVs are again categorical, but there is no clustering (SimpleHmm) - The version of MixtureDirichlet but with temporal dependency (HmmMixtureRNA) Coming soon: - A linear model in the emission that can account for known covariates - The equivalent of MixtureGaussian but with CNVs as Categorical random variable - A model on genes (all the other models assume a division in segments) To install: `$ pip install congas` To run a simple analysis on the example data ```python import congas as cn from congas.models import MixtureGaussian data_dict = cn.simulation_data params, loss = cn.run_analysis(data_dict,MixtureGaussian, steps=200, lr=0.05) ``` [Full Documentation](https://annealpyro.readthedocs.io/en/latest/) %package help Summary: Development documents and examples for congas Provides: python3-congas-doc %description help # Copy number genotyping from scRNA sequencing [![Build Status](https://travis-ci.org/Militeee/anneal.svg?branch=master)](https://travis-ci.org/Militeee/congas) [![codecov](https://codecov.io/gh/Militeee/anneal/branch/master/graph/badge.svg)](https://codecov.io/gh/Militeee/congas) A set of Pyro models and functions to infer CNA from scRNA-seq data. It comes with a companion R package (hlink) that works as an interface and provides preprocessing, simulation and visualization routines. Currently providing: - A mixture model on segments where CNV are modelled as LogNormal random variable (MixtureGaussian) - Same as above but the number of cluster is learned (MixtureGaussianDMP) - A model where CNVs are modelled as outcome from Categorical distributions, clusters share the same parameters (MixtureDirichlet) - A simple Hmm where CNVs are again categorical, but there is no clustering (SimpleHmm) - The version of MixtureDirichlet but with temporal dependency (HmmMixtureRNA) Coming soon: - A linear model in the emission that can account for known covariates - The equivalent of MixtureGaussian but with CNVs as Categorical random variable - A model on genes (all the other models assume a division in segments) To install: `$ pip install congas` To run a simple analysis on the example data ```python import congas as cn from congas.models import MixtureGaussian data_dict = cn.simulation_data params, loss = cn.run_analysis(data_dict,MixtureGaussian, steps=200, lr=0.05) ``` [Full Documentation](https://annealpyro.readthedocs.io/en/latest/) %prep %autosetup -n congas-0.0.75 %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-congas -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 0.0.75-1 - Package Spec generated