Metadata-Version: 2.1
Name: scCODA
Version: 0.0.1
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: MIT
Description: 
        # 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.
        
        The statistical methodology and benchmarking performance are described in:
         
        *scCODA: A Bayesian model for compositional single-cell data analysis (Ostner et al., 2020)*
        (Code for the article available at 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`
        
        
        
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
