scCODA - Single-cell differential composition analysis

Note This implementation is no longer maintained. A new version in Jax is available in pertpy.

For more information and contribution guidelines please visit the associated Github repository: https://github.com/theislab/pertpy

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 cell-type annotated data directly from scanpy and other sources. Aside from the scCODA model (Büttner, Ostner et al (2021)), the package also allows the easy application of other differential testing methods.

The statistical methodology and benchmarking performance are described in:

Büttner, Ostner et al (2021). scCODA is A Bayesian model for compositional single-cell data analysis (Nature Communications)

Code for reproducing the analysis from the paper is available here.

For further information on the scCODA package and model, please refer to the documentation and the tutorials.

Installation

Running the package requires a working Python environment (>=3.8).

This package uses the tensorflow (>=2.8) and tensorflow-probability (>=0.16) packages. The GPU computation features 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 that you want to install 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

Docker container:

We provide a Docker container image for scCODA (https://hub.docker.com/repository/docker/wollmilchsau/scanpy_sccoda).

Usage

Import scCODA in a Python session via:

import sccoda

Tutorials

scCODA provides a number of tutorials for various purposes. Please also visit the documentation for further information on the statistical model, data structure and API.

• The "getting started" tutorial provides a quick-start guide for using scCODA.

• In the advanced tutorial, options for model specification, diagnostics, and result interpretation are disccussed.

• The data import and visualization tutorial focuses on loading data from different sources and visualizing their characteristics.

• The tutorial on other methods explains how to apply other methods for differential abundance testing from within scCODA.