Spatial Transcriptomics Prediction from Histology Images at Single-cell Resolution using RedeHist
Yunshan Zhong, Jiaxiang Zhang, Xianwen Ren · bioRxiv (Cold Spring Harbor Laboratory) · 2024
Abstract Spatial transcriptomics (ST) offers substantial promise in elucidating the tissue architecture of biological systems. However, its utility is frequently hindered by constraints such as high costs, time-intensive procedures, and incomplete gene readout. Here we introduce RedeHist, a novel deep learning approach integrating scRNA-seq data to predict ST from histology images at single-cell resolution. Application of RedeHist to both sequencing-based and imaging-based ST data demonstrated its outperformance in high-resolution and accurate prediction, whole-transcriptome gene imputation, and fine-grained cell annotation compared with the state-of-the-art algorithms.