gnSPADE: Incorporating Gene Network Structures Enhances Reference‐Free Deconvolution in Spatial Transcriptomics

Spatial transcriptomics (ST) technologies offer unprecedented insights into the spatial organization of gene expression, allowing for the study of tissue architecture, domain boundaries, and cell–cell interactions. However, most ST data generated so far are at multicellular resolution, where each spot captures transcripts from a mixture of diverse cells of different cell types. While reference‐based deconvolution approaches offer robust solutions, they rely heavily on the availability and quality of external single‐cell reference data, which may be incomplete, unavailable, or poorly matched to the spatial data. Moreover, even when such references are available, they often represent only broad cell types, potentially obscuring finer subpopulation structures and masking intra‐type heterogeneity. To overcome these limitations, we introduced gnSPADE, a reference‐free spatial deconvolution method that incorporates gene network structures via a Markov random field within a latent Dirichlet allocation modeling framework. gnSPADE jointly infers cell type‐specific transcriptional profiles and spatial compositions without external references. When applied to synthetic and real ST datasets, gnSPADE achieves improved accuracy, spatial resolution, and biological interpretability compared to other methods, highlighting the power of reference‐free deconvolution in resolving complex tissues.