Completing Spatial Transcriptomics Data for Gene Expression Prediction Benchmarking

TL;DR

This paper introduces SpaRED, a comprehensive benchmark dataset for gene expression prediction from histology images, and SpaCKLE, a transformer-based model that significantly improves prediction accuracy, advancing research in Spatial Transcriptomics.

Contribution

The paper presents SpaRED, a curated dataset for standardized evaluation, and SpaCKLE, a novel transformer-based model that outperforms existing methods in gene expression completion.

Findings

SpaCKLE reduces mean squared error by over 82.5%.

Benchmarking eight models shows SpaCKLE's superior performance.

SpaRED enables fair comparison and accelerates Spatial Transcriptomics research.

Abstract

Spatial Transcriptomics is a groundbreaking technology that integrates histology images with spatially resolved gene expression profiles. Among the various Spatial Transcriptomics techniques available, Visium has emerged as the most widely adopted. However, its accessibility is limited by high costs, the need for specialized expertise, and slow clinical integration. Additionally, gene capture inefficiencies lead to significant dropout, corrupting acquired data. To address these challenges, the deep learning community has explored the gene expression prediction task directly from histology images. Yet, inconsistencies in datasets, preprocessing, and training protocols hinder fair comparisons between models. To bridge this gap, we introduce SpaRED, a systematically curated database comprising 26 public datasets, providing a standardized resource for model evaluation. We further propose SpaCKLE, a state-of-the-art transformer-based gene expression completion model that reduces mean squared error by over 82.5% compared to existing approaches. Finally, we establish the SpaRED benchmark, evaluating eight state-of-the-art prediction models on both raw and SpaCKLE-completed data, demonstrating SpaCKLE substantially improves the results across all the gene expression prediction models. Altogether, our contributions constitute the most comprehensive benchmark of gene expression prediction from histology images to date and a stepping stone for future research on Spatial Transcriptomics.