# ClustAssessPy
[](https://pepy.tech/project/ClustAssessPy)
[](https://pepy.tech/project/ClustAssessPy)
ClustAssessPy offers a data-driven approach for optimizing parameter values across all stages of graph-based community detection clustering in single-cell datasets. This Python package is a lighter adaptation of ClustAssess (R) [1], incorporating its main functions and can be used by the Scanpy community to guide robust clustering through data-driven selection in all community detection clustering steps:
- **Dimensionality Reduction:** Selection of the base embedding (UMAP vs PCA) and the number and type of features (e.g., highly-variable vs most abundant).
- **Graph Type:** Choice of graph type for the adjacency matrix (nearest neighbors vs shared nearest neighbors) and the number of neighbors.
- **Clustering:** Identification of the most stable algorithm (Leiden or Louvain) and the appropriate resolution value.
## Installation
ClustAssessPy requires Python 3.7 or newer.
### Dependencies
- numpy
- pandas
- scanpy
- umap-learn
- seaborn
- matplotlib
- scipy
- networkx
- plotnine
- pynndescent
- leidenalg
- louvain
- igraph
### User Installation
We recommend that you download ClustAssessPy on a virtual environment (venv or Conda).
```sh
pip install ClustAssessPy
```
## Getting Started
Documentation for the main functions is available [here](https://core-bioinformatics.github.io/ClustAssessPy/ClustAssessPy.html). For a detailed tutorial, click [here](https://github.com/Core-Bioinformatics/ClustAssessPy/blob/main/Examples/tutorial_cuomo.ipynb).
## References
[1] Shahsavari, A., Munteanu, A., & Mohorianu, I. (2022). ClustAssess: Tools for Assessing the Robustness of Single-Cell Clustering. https://doi.org/10.1101/2022.01.31.478592
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"description": "# ClustAssessPy\n\n[](https://pepy.tech/project/ClustAssessPy)\n[](https://pepy.tech/project/ClustAssessPy)\n\nClustAssessPy offers a data-driven approach for optimizing parameter values across all stages of graph-based community detection clustering in single-cell datasets. This Python package is a lighter adaptation of ClustAssess (R) [1], incorporating its main functions and can be used by the Scanpy community to guide robust clustering through data-driven selection in all community detection clustering steps:\n\n- **Dimensionality Reduction:** Selection of the base embedding (UMAP vs PCA) and the number and type of features (e.g., highly-variable vs most abundant).\n- **Graph Type:** Choice of graph type for the adjacency matrix (nearest neighbors vs shared nearest neighbors) and the number of neighbors.\n- **Clustering:** Identification of the most stable algorithm (Leiden or Louvain) and the appropriate resolution value.\n\n## Installation\n\nClustAssessPy requires Python 3.7 or newer.\n\n### Dependencies\n\n- numpy\n- pandas\n- scanpy\n- umap-learn\n- seaborn\n- matplotlib\n- scipy\n- networkx\n- plotnine\n- pynndescent\n- leidenalg\n- louvain\n- igraph\n\n### User Installation\n\nWe recommend that you download ClustAssessPy on a virtual environment (venv or Conda).\n\n```sh\npip install ClustAssessPy\n```\n\n## Getting Started\n\nDocumentation for the main functions is available [here](https://core-bioinformatics.github.io/ClustAssessPy/ClustAssessPy.html). For a detailed tutorial, click [here](https://github.com/Core-Bioinformatics/ClustAssessPy/blob/main/Examples/tutorial_cuomo.ipynb).\n\n## References\n\n[1] Shahsavari, A., Munteanu, A., & Mohorianu, I. (2022). ClustAssess: Tools for Assessing the Robustness of Single-Cell Clustering. https://doi.org/10.1101/2022.01.31.478592\n",
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