scCODA 0.1.1.post1

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

Running the package requires a working Python environment (>=3.7).

This package uses the tensorflow (==2.3.2) and tensorflow-probability (==0.11.0) packages. The GPU versions of these packages have not been tested with scCODA and are thus not recommended.

To install scCODA via pip, call:

pip install sccoda

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

• Install the package:

  python setup.py install

Import scCODA in a Python session via:

import sccoda