Single cell clustering at a glance. Identify which clustering resolution(s) fit your data within minutes.

PyClustree offers:

• Automated clustering (leiden/louvain) at various resolutions
• Automatic selection of significant resolutions
• Visualization of clusters per resolution and their relative compositions
• Easy to use, yet highly customizable Python API
• Cluster recoloring for PyClustree and UMAP visualization (see below)

git clone https://github.com/siebrenf/pyclustree.git
pip install pyclustree

git clone https://github.com/siebrenf/pyclustree.git
conda env create -n pyclustree -f pyclustree/requirements.yaml
conda activate pyclustree
pip install --editable . --no-deps --ignore-installed

The UMAPs and cluster tree can be compared immediately due to the automatic renaming and recoloring of the clusters. Without renaming and recoloring, figures would have looked like this:

For example, note how cluster 2 becomes cluster 3 at resolution 0.53.

This tool was inspired by:

• The original Clustree R package.
• This BioStars post by firestar.