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%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.nju.edu.cn/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
[](https://travis-ci.org/Militeee/congas)
[](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
[](https://travis-ci.org/Militeee/congas)
[](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
[](https://travis-ci.org/Militeee/congas)
[](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
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.75-1
- Package Spec generated
|
