densitree

A reference Python implementation of the SPADE algorithm for high-dimensional cytometry and single-cell data.

SPADE (Spanning-tree Progression Analysis of Density-normalized Events) extracts cellular hierarchies from high-dimensional single-cell data by combining density-dependent downsampling, agglomerative clustering, and minimum spanning tree construction.

---

Why densitree?

• scikit-learn compatible — fit() / fit_predict() API, works with numpy arrays and pandas DataFrames

• Extensible pipeline — swap any step (density estimation, clustering, etc.) via the BaseStep interface

• Dual visualization — static matplotlib and interactive plotly backends

• Reproducible — deterministic results with random_state parameter

• Well-tested — comprehensive unit and integration test suite

• Pure Python — no R or MATLAB dependency

Quick Example

import numpy as np
from densitree import SPADE

X = np.random.default_rng(0).normal(size=(1000, 10))

spade = SPADE(n_clusters=20, downsample_target=0.1, random_state=42)
spade.fit(X)

# Cluster labels for all 1000 cells
print(spade.labels_)

# Per-cluster statistics
print(spade.result_.cluster_stats_)

# Visualize the SPADE tree
spade.result_.plot_tree(color_by=0, backend="matplotlib")

Example outputs

Tree colored by marker expression

Nodes are sized by cell count. Color shows median CD3 expression — high (yellow) in T cell clusters, low (purple) elsewhere.

Condition comparison

Red nodes are enriched in the disease condition, blue in healthy. Cluster 5 (dark red) contains a rare population expanded in disease.

Cluster heatmap

Median marker expression per cluster reveals distinct cell populations.

Interactive visualization

densitree also supports interactive plotly trees — hover for cluster details, zoom and pan.

Installation

pip install densitree

Or from source:

git clone https://github.com/fuzue/densitree.git
cd densitree
pip install -e ".[dev]"

---