Final Report — RAG-ST: Retrieval-Augmented Generation for Spatial Transcriptomics

Sun, 28 Sep 2025 00:00:00 +0000

Hello! I’m Zeyu Zou! I have been contributing to the RAG-ST: Retrieval-Augmented Generation for Spatial Transcriptomics project under the mentorship of Ziheng Duan. My project focuses on developing a framework that predicts spatial gene expression from histology images by combining vision encoders with single-cell RNA-seq references. The goal is to make spatial transcriptomics more affordable, interpretable, and scalable for the research community.

Introduction

RAG-ST is designed to reduce the cost and complexity of spatial transcriptomics by leveraging existing histology images and scRNA-seq priors. This work integrates computer vision with retrieval-augmented generation to improve prediction accuracy and interpretability.

Methods

The project used a two-stage pipeline:

Vision encoder (ResNet50/ViT) to map histology patches to cell type distributions.
Retrieval-augmented generation guided by scRNA-seq profiles to predict gene expression.

Datasets included HEST-1K (paired histology and expression) and CellxGene Census as the reference database. Training and evaluation pipelines were implemented in PyTorch.

Results

Implemented a complete pipeline from histology preprocessing to expression prediction.
Achieved higher correlation scores (Pearson/Spearman) and lower errors (MSE/MAE) compared to baseline models.
Produced spatial gene expression maps with interpretable retrieval traces and attention weights.
Released open-source code, preprocessing scripts, and analysis notebooks for reproducibility.

Future Work

Extend experiments to additional tissues (lung, liver, tumor samples).
Test cross-dataset generalization and robustness.
Explore integration into clinical pathology workflows for affordable spatial inference.

Acknowledgments

Thanks to my mentor Ziheng Duan, the UC OSPO team, the HEST-1K dataset contributors, and the CellxGene Census project. This work was conducted under OSRE 2025.

RAG-ST: Retrieval-Augmented Generation for Spatial Transcriptomics

Thu, 19 Jun 2025 00:00:00 +0000

Hi everyone! My name is Zeyu, and I will be working on a project for a retrieval-enhanced generative framework for spatial transcriptomics during Google Summer of Code 2025. My project is called RAG-ST: Retrieval-Augmented Generation for Spatial Transcriptomics and is supervised by Ziheng Duan. The goal is to develop a retrieval-enhanced generative framework for predicting spatial gene expression from histological images, making spatial transcriptomics more affordable and easier to implement. You can view my full proposal here!

Spatial transcriptomics enables the capture of gene expression profiles with spatial resolution, providing unprecedented insights into cellular organization and the tissue microenvironment. However, its widespread application is limited by high costs and technical complexity. In contrast, histological imaging is inexpensive and widely accessible. If we can accurately predict gene expression from histology images, then high-resolution spatial information can be inferred without costly experiments.

My project will:

Create a large-scale paired dataset combining HEST histology images with reference gene expression profiles from CellxGene.
Design a novel RAG-ST architecture that enables both interpretable and controllable generation of spatial gene expression.
Benchmark RAG-ST against current state-of-the-art models for image-based gene expression inference.
Open-source the full codebase and provide comprehensive tutorials to support future research and development.

I am excited to contribute to this project and help broaden access to spatial transcriptomics insights through machine learning–powered predictions!

Zeyu Zou

University of Northeastern Graduate

Zeyu Zou is a graduate student at the University of Northeastern, where he is majoring in Analytics.

Zeyu Zou | UCSC OSPO