PySpace

PySpace is a Python implementation of SPACE, Spatial Patterning Analysis of Cellular Ensembles. It provides spatial census, cisMI/transMI, pattern learning, diversity analysis, visualization, and Python-native workflow tools for multiplex tissue imaging.

Scientific compatibility is pinned to upstream SPACE commit 94f0a0f9311e2ee2b406ebc45c84a4e93f2f38f8. Numerical and tabular parity is tested against live R-generated oracles; Python-only interfaces delegate to the same computational core.

Installation

python -m pip install pyspace-analysis

Optional GPL-licensed community-detection backends are separate:

python -m pip install 'pyspace-analysis[community]'

Quick start

import pandas as pd
from pyspace import SpacePipeline

cells = pd.DataFrame(
    {
        "X": [0.0, 1.0, 2.0],
        "Y": [0.0, 0.0, 0.0],
        "Object": [1, 1, 2],
    }
)

pipeline = SpacePipeline().load_table(cells, validate=True)
result = pipeline.census(radii=[1.0], n_neighborhoods=1)

The project is under active pre-1.0 development. See the versioned documentation for the R-to-Python API map, reproducibility contract, and complete examples.

Data policy

This repository contains source code, synthetic fixtures, and provenance-reviewed derived tutorial outputs only. Clinical spreadsheets, raw images, annotations, manuscripts, and generated study results are prohibited.

License and attribution

Apache-2.0. See LICENSE and NOTICE. PySpace is not the canonical R SPACE package; cite the original SPACE project and the PySpace release used in an analysis.