DSCT: a novel deep-learning framework for rapid and accurate spatial transcriptomic cell typing.

ABSTRACT: Unraveling 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 data sets. 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 ty

SUBMITTER: Xu Y 

PROVIDER: S-EPMC12045154 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature