HIDF: Integrating Tree-Structured scRNA-seq Heterogeneity for Hierarchical Deconvolution of Spatial Transcriptomics

The limited spatial resolution of mainstream spatial transcriptomic technologies captures transcriptomic mixtures from multiple cells per spot, obscuring crucial single-cell information. While numerous methods leverage single-cell RNA sequencing references to infer cellular composition from ST data, they primarily rely on fixed cell type labels, overlooking the intrinsic hierarchical heterogeneity (subtypes within broad types) of cellular populations and its association with spatial organization. To address this limitation, HIDF, a Hierarchical Iterative Deconvolution Framework is proposed. HIDF progressively resolves cellular heterogeneity from coarse to fine granularity, it employs a hierarchical iterative optimization mechanism guided by the cluster-tree to recover single-cell spatial distributions. This process is further stabilized and enhanced by incorporating dual regularization constraints (spatial neighborhood and cross-level regularization). Comprehensive benchmarking demonstrates that HIDF outperforms existing methods on simulated and real tissue datasets. In addition, HIDF not only reveals cell type distributions consistent with known tissue functions but also uncovers spatially heterogeneous patterns of cell subtypes undetectable by conventional methods.