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

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:

1. Vision encoder (ResNet50/ViT) to map histology patches to cell type distributions.
2. 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.

Links

• Repository: https://github.com/ZeyuZou/rag-st
• Preprint: RAG-ST: Retrieval-Augmented Generation for Spatial Transcriptomics (bioRxiv, 2025)