THItoGene: a deep learning method for predicting spatial transcriptomics from histological images.
IF: 13.994


Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at


Spatial Transcriptomics
capsule network
histopathological images
spatial transcriptomics

MeSH terms

Artificial Intelligence
Carcinoma, Squamous Cell
Deep Learning
Skin Neoplasms
Gene Expression Profiling


Jia, Yuran
Liu, Junliang
Chen, Li
Zhao, Tianyi
Wang, Yadong