slot-toolkit 0.1.0

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
• pip 23.0 or higher (upgrade with pip install --upgrade pip)

Steps

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.

Tutorial

Here we present our SLOT score analysis on the stage IV oocyte protein dataset. This tutorial demonstrates how to identify spatial polarity proteins at subcellular resolution. The processed data are available at XenoSTAR.