SLOT is an optimal-transport–based machine learning framework for quantifying and modeling the spatial–temporal localization of intracellular molecules.
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.
- Spatial localization polarity quantification
- Location patterns matching
- Subcellular location clustering
- Spatial-temporal co-localization detection
- Python 3.10 or higher
- pip (Python package installer)
- scipy
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Clone the repository:
cd ~ git clone https://github.com/Lifeomics/SLOT.git cd SLOT
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Create a conda environment and activate it:
conda create --name SLOT_env python=3.10 conda activate SLOT_env
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Install the required packages and SLOT:
pip install -e .
It may takes 1-2 mins to finish this installation
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.