KineticST

Kinetic-theory framework for multiscale modelling of spatial transcriptomics.

Bridges microscopic cell-cell interactions to macroscopic tissue organisation via a Boltzmann-like collision operator on latent cell states, integrated on a spatial graph with stochastic dynamics and invariant-measure analysis.

Installation

pip install -e .

Quick start

import numpy as np
from kinetic_st import KineticST, KineticSTConfig, generate_kinetic_simulation

# Synthetic data
sim = generate_kinetic_simulation(n_types=6, n_spots=1600, seed=42)

# Fit
model = KineticST(KineticSTConfig(n_steps=150, latent_dim=10, verbose=True))
model.fit(sim.Y, sim.coords)

# Results
labels = model.get_domain_labels()
field = model.get_expression_field()
print(model.summary())

Pipeline stages

1. Latent embedding — PCA on log-CPM expression to a low-dimensional cell-state space
2. Spatial graph — kNN adjacency from coordinates
3. Collision kernel — ligand-receptor rule with irreversible attractor bias
4. Kinetic integration — deterministic (Boltzmann), stochastic (Euler-Maruyama), or time-sliced solver
5. Moment closure — macroscopic tissue expression fields via local KDE moments
6. Invariant measure — non-equilibrium steady-state estimation (KDE or GMM)
7. Spatial domains — segmentation via local invariant-measure divergence
8. Supervised markers — one-vs-rest z-scores on the dynamics-smoothed field

License

MIT