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 ClustAssessPyDocumentation 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