slot-toolkit 1.0.2

A package for Subcellular Location Optimal Transport (SLOT) algorithm.

Subcellular Location Optimal Transport (SLOT)

SLOT is an optimal-transport–based machine learning framework for quantifying and modeling the spatial–temporal localization of intracellular molecules.

Introduction

By integrating subcellular-resolution spatial transcriptomics (mRNA) and proteomics (protein) datasets, SLOT systematically aligns and compares molecular distributions across cellular compartments and temporal stages. The framework infers relocation trajectories and quantifies dynamic shifts in subcellular localization patterns. As a comprehensive computational toolbox, SLOT enables systematic modeling of subcellular molecular spatial polarity, supporting pattern detection, spatial-location clustering investigations and spatiotemporal dynamic analysis.

Features

• Spatial localization polarity quantification
• Location patterns matching
• Subcellular location clustering
• Spatial-temporal co-localization detection

Installation

Prerequisites

• Python 3.10 or higher

Install from PyPI (Recommended)

pip install slot-toolkit

Install from Source

1. Clone the repository:

   git clone https://github.com/Lifeomics/SLOT.git
   cd SLOT
   

2. Create a conda environment and activate it:

   conda create --name SLOT_env python=3.10
   conda activate SLOT_env
   

3. Install SLOT and its dependencies:

   pip install .
   

   For development / editable install (changes to source take effect immediately):

   pip install -e .
   

Installation typically takes 1–2 minutes.

Quick Start

import SLOT

# Load your spatial omics data
adata = SLOT.data.load_data("path/to/data.h5ad")

# Compute SLOT polarity scores
scores = SLOT.model.compute_slot_score(adata)

# Cluster subcellular localization patterns
SLOT.cluster.run_clustering(adata)

# Visualize results
SLOT.plot.bindingplot(adata)

Tutorials

Tutorial	Description
Tutorial 1: SLOT Score	Identify spatial polarity proteins at subcellular resolution
Tutorial 2: SLOT Cluster	Cluster subcellular localization patterns
Tutorial 3: Pattern Matching	Match and compare spatial distribution patterns

The processed data used in tutorials are available at XenoSTAR.

Citation

If you use SLOT in your research, please cite our paper (coming soon).

License

This project is licensed under the MIT License - see the LICENSE file for details.