Completing Spatial Transcriptomics Data for Gene Expression Prediction Benchmarking
Journal:
arXiv
Published Date:
May 5, 2025
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.
