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Related Experiment Video

Updated: Jan 9, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Diffusion-based Representation Integration for Foundation Models Improves Spatial Transcriptomics Analysis.

Atishay Jain¹, Tuan M Pham², David H Laidlaw¹

¹Department of Computer Science, Brown University, 115 Waterman Street, 02912, RI, United States.

Biorxiv : the Preprint Server for Biology

|December 3, 2025

Summary

This summary is machine-generated.

DRIFT integrates spatial information into single-cell foundation models using spatial transcriptomics data. This framework enhances cell-type annotation and clustering by leveraging spatial graphs and heat kernel diffusion.

Keywords:

Deep Learning Foundation Models Graph Diffusion Spatial Transcriptomics

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics (ST) preserves gene expression and spatial context.
Existing foundation models for single-cell RNA sequencing (scRNA-seq) lack spatial information.
Few foundation models are optimized for ST data, limiting generalizability across tasks.

Purpose of the Study:

To propose DRIFT, a framework integrating spatial information into single-cell foundation models.
To leverage spatial graphs from ST data and heat kernel diffusion for enhanced embeddings.
To improve the performance of foundation models on ST data analysis tasks.

Main Methods:

Developed DRIFT framework using spatial graphs from ST data.
Applied heat kernel diffusion to propagate embeddings across spatial neighborhoods.
Benchmarked five foundation models (scRNA-seq and ST-based) on ST tasks: annotation, alignment, and clustering.

Main Results:

Spatial diffusion significantly improved the performance of existing single-cell foundation models on ST data.
DRIFT outperformed specialized state-of-the-art methods for ST data analysis.
Demonstrated enhanced performance in cell-type annotation, clustering, and cross-sample alignment.

Conclusions:

DRIFT is an effective and generalizable framework for modeling spatial transcriptomics.
The framework bridges the gap towards universal models for single-cell analysis.
Spatial diffusion enhances the utility of foundation models for ST data.