THItoGene: a deep learning method for predicting spatial transcriptomics from histological images.
Yuran JiaJunliang LiuLi ChenTianyi ZhaoYadong WangPublished in: Briefings in bioinformatics (2023)
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 https://github.com/yrjia1015/THItoGene.
Keyphrases
- deep learning
- artificial intelligence
- gene expression
- convolutional neural network
- single cell
- neural network
- squamous cell carcinoma
- dna methylation
- machine learning
- big data
- high resolution
- rna seq
- optical coherence tomography
- endothelial cells
- cell therapy
- radiation therapy
- young adults
- mass spectrometry
- risk assessment
- health information
- induced pluripotent stem cells
- clinical evaluation