ScHDeepInsight: a hierarchical deep learning framework for precise immune cell annotation in single-cell RNA-seq data.

ABSTRACT: Accurate classification of immune cells is crucial for elucidating their diverse roles in health and disease. However, this task remains very challenging in single-cell RNA sequencing (scRNA-seq) data due to the complex and hierarchical relationships of immune cell types. To address this, we introduce scHDeepInsight, a deep learning framework that extends our previous scDeepInsight model by integrating a biologically-informed classification architecture with an adaptive hierarchical focal loss (AHFL). The framework builds on our established method of converting gene expression data into two-dimensional structured images, enabling convolutional neural networks to effectively capture both global and fine-grained transcriptomic features. This design utilizes hierarchical relationships among i

SUBMITTER: Jia S 

PROVIDER: S-EPMC12502058 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature