Metadata-Version: 2.1
Name: scCODA
Version: 0.1
Summary: A Dirichlet-Multinomial approach to identify compositional changes in count data.
Home-page: https://github.com/theislab/scCODA
Author: Johannes Ostner, Benjamin Schubert
Author-email: johannes.ostner@helmholtz-muenchen.de
License: BSD
Keywords: RNA,single cell,composition,CODA,compositional analysis
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: tensorflow
Requires-Dist: tensorflow-probability
Requires-Dist: arviz
Requires-Dist: seaborn
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: scanpy
Requires-Dist: anndata
Requires-Dist: patsy
Requires-Dist: sklearn
Requires-Dist: statsmodels
Requires-Dist: scikit-bio
Requires-Dist: rpy2


# scCODA - Single-cell differential composition analysis 
scCODA allows for identification of compositional changes in high-throughput sequencing count data, especially cell compositions from scRNA-seq.
It also provides a framework for integration of results directly from *scanpy* and other sources.

![scCODA](.github/Figures/Fig1_v10.png)

The statistical methodology and benchmarking performance are described in:

Büttner, Ostner *et al.* (2020). **scCODA: A Bayesian model for compositional single-cell data analysis**


[Link](https://www.biorxiv.org/content/10.1101/2020.12.14.422688v1) to article on *BioRxiv*.
Code for reproducing the article is available [here](https://github.com/theislab/scCODA_reproducibility).

For further information, please refer to the 
[documentation](https://scdcdm-public.readthedocs.io/en/latest/) and the 
[tutorials](https://github.com/theislab/SCDCdm/blob/master/tutorials/Tutorial.ipynb).

## Installation

A functioning python environment (>=3.7) is necessary to run this package.

This package uses the tensorflow (>=2.1.0) and tensorflow-probability (>=0.9.0) packages.
The GPU versions of these packages have not been tested with scCODA and are thus not recommended.

To install scCODA from source:

- Navigate to the directory you want scCODA in
- Clone the repository from [Github](https://github.com/theislab/scCODA):

    `git clone https://github.com/theislab/scCODA`

- Navigate to the root directory of scCODA:

    `cd scCODA`

- Install dependencies:

    `pip install -r requirements.txt`

Import scCODA in a Python session via:

    `import sccoda`


