A Dirichlet-Multinomial approach to identify compositional changes in count data.
Project 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:
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:
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Navigate to the directory you want scCODA in
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Clone the repository from Github:
git clone https://github.com/theislab/scCODA
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Navigate to the root directory of scCODA:
cd scCODA
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Install dependencies:
pip install -r requirements.txt
Import scCODA in a Python session via:
`import sccoda`
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