scCODA 0.1.1

A Dirichlet-Multinomial approach to identify compositional changes in count data.

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:

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

Link to article on BioRxiv. Code for reproducing the article is available here.

For further information, please refer to the documentation and the tutorials.

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:

  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`