DSCT: A novel deep learning framework for rapid and accurate spatial transcriptomic cell typing

Abstract Unraveling the complex cell-type composition and gene expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep Neural Network-based Spatial Cell Typing (DSCT), an innovative framework for spatial cell typing within spatial transcriptomic datasets. This approach utilizes a synergistic integration of an enhanced gene selection strategy and a lightweight deep neural network for data training, offering a more rapid and accurate solution for the analysis of spatial transcriptomic data. Based on comprehensive analysis, DSCT achieved exceptional accuracy in cell-type identification across various brain regions, species, and spatial transcriptomic platforms. It also performed well in mapping finer cell types, thereby showcasing its versatility and adaptability across diverse datasets. Strikingly, DSCT exhibited high efficiency and remarkable processing speed, with fewer computational resource demands. As such, this novel approach opens new avenues for exploring the spatial organization of cell types and gene expression patterns, advancing our understanding of biological functions and pathologies within the nervous system.