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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# scCODA - Single-cell differential composition analysis\n\n\u003e **Note**\n\u003e This implementation is no longer maintained. A new version in Jax is available in [pertpy](https://pertpy.readthedocs.io/en/latest).\n\u003e\n\u003e For more information and contribution guidelines please visit the associated Github repository: https://github.com/theislab/pertpy\n\nscCODA allows for identification of compositional changes in high-throughput sequencing count data, especially cell compositions from scRNA-seq.\nIt also provides a framework for integration of cell-type annotated data directly from [scanpy](https://scanpy.readthedocs.io/en/stable/) and other sources.\nAside from the scCODA model (Büttner, Ostner *et al* (2021)), the package also allows the easy application of other differential testing methods.\n\n![scCODA](.github/Figures/Figure1.png)\n\nThe statistical methodology and benchmarking performance are described in:\n \nBüttner, Ostner *et al* (2021). **scCODA is A Bayesian model for compositional single-cell data analysis**\n([*Nature Communications*](https://www.nature.com/articles/s41467-021-27150-6))\n\nCode for reproducing the analysis from the paper is available [here](https://github.com/theislab/scCODA_reproducibility).\n\nFor further information on the scCODA package and model, please refer to the \n[documentation](https://sccoda.readthedocs.io/en/latest/) and the \n[tutorials](https://github.com/theislab/scCODA/blob/master/tutorials).\n\n## Installation\n\nRunning the package requires a working Python environment (\u003e=3.8).\n\nThis package uses the `tensorflow` (`\u003e=2.8`) and `tensorflow-probability` (`\u003e=0.16`) packages.\nThe GPU computation features of these packages have not been tested with scCODA and are thus not recommended.\n    \n**To install scCODA via pip, call**:\n\n    pip install sccoda\n\n\n**To install scCODA from source**:\n\n- Navigate to the directory that you want to install scCODA in\n- Clone the repository from Github (https://github.com/theislab/scCODA):\n\n    `git clone https://github.com/theislab/scCODA`\n\n- Navigate to the root directory of scCODA:\n\n    `cd scCODA`\n\n- Install dependencies::\n\n    `pip install -r requirements.txt`\n\n- Install the package:\n\n    `python setup.py install`\n\n**Docker container**:\n\nWe provide a Docker container image for scCODA (https://hub.docker.com/repository/docker/wollmilchsau/scanpy_sccoda).\n\n## Usage\n\nImport scCODA in a Python session via:\n\n    import sccoda\n\n**Tutorials**\n\nscCODA provides a number of tutorials for various purposes. Please also visit the [documentation](https://sccoda.readthedocs.io/en/latest/) for further information on the statistical model, data structure and API.\n\n- The [\"getting started\" tutorial](https://sccoda.readthedocs.io/en/latest/getting_started.html) provides a quick-start guide for using scCODA.\n\n- In the [advanced tutorial](https://sccoda.readthedocs.io/en/latest/Modeling_options_and_result_analysis.html), options for model specification, diagnostics, and result interpretation are disccussed.\n\n- The [data import and visualization tutorial](https://sccoda.readthedocs.io/en/latest/Data_import_and_visualization.html) focuses on loading data from different sources and visualizing their characteristics.\n\n- The [tutorial on other methods](https://sccoda.readthedocs.io/en/latest/using_other_compositional_methods.html) explains how to apply other methods for differential abundance testing from within scCODA.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheislab%2Fsccoda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheislab%2Fsccoda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheislab%2Fsccoda/lists"}