Metadata-Version: 2.1
Name: congas
Version: 0.0.64
Summary: Copy Number genotyping from single cell RNA sequencing
Home-page: https://github.com/Militeee/congas
Author: Salvatore Milite
Author-email: militesalvatore@gmail.com
License: GPL-3.0
Keywords: scRNA scDNA RNA CNV CNA Cancer Copy-number Bioinformatics
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Dist: matplotlib (>=3.1)
Requires-Dist: pandas (>=1.0)
Requires-Dist: pyro-ppl (>=1.5)
Requires-Dist: numpy (>=1.18)
Requires-Dist: scikit-learn

# 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/)


