Journal Article

STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning

Zhou, Jia-Yi, Zhang, Shihua, Gao, Chun-Chun, Yang, Ying, Yang, Yun-Gui, Cui, Guanshen, Wu, Ying, Zhao, Yong-Liang, Yao, Bofei

2024

Overview

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Accuracy / Advances in Spatial Transcriptomics for Understanding Development and Disease / Animal Genetics and Genomics / Animals / Annotations / Bioinformatics / Biomedical and Life Sciences / Cell annotation / data collection / Datasets / Deep Learning / Evolutionary Biology / Gene expression / Gene Expression Profiling - methods / genome / histology / Human Genetics / Humans / Imputation / Life Sciences / Method / Microbial Genetics and Genomics / Morphology / Multimodal data integration / Plant Genetics and Genomics / Spatial discrimination learning / Spatial transcriptomics / Transcriptome / Transcriptomics