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Showing content from https://github.com/Core-Bioinformatics/ClustAssessPy below:

GitHub - Core-Bioinformatics/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.

ClustAssessPy requires Python 3.7 or newer.

• numpy
• pandas
• scanpy
• umap-learn
• seaborn
• matplotlib
• scipy
• networkx
• plotnine
• pynndescent
• leidenalg
• louvain
• igraph

We recommend that you download ClustAssessPy on a virtual environment (venv or Conda).

pip install ClustAssessPy

Documentation for the main functions is available here. For a detailed tutorial, click here.

[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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