Abstract
Most spatial transcriptomics (ST) technologies (e.g. 10× Visium) operate at the multicellular level, where each spatial location often contains a mixture of cells with heterogeneous cell types. Thus, effective deconvolution of cell type compositions is critical for downstream analysis. Although reference-based deconvolution methods have been proposed, they depend on the availability of reference data, which may not always be accessible. Additionally, within a deconvolved cell type, cellular heterogeneity may still exist, requiring further deconvolution to uncover finer structures for a better understanding of this complexity. Here, we present gwSPADE, a gene frequency-weighted reference-free SPAtial DEconvolution method for ST data. gwSPADE requires only the gene count matrix and utilizes appropriate weighting schemes within a topic model to accurately recover cell type transcriptional profiles and their proportions at each spatial location, without relying on external single-cell reference information. In various simulations and real data analyses, gwSPADE demonstrates scalability across various platforms and shows superior performance over existing reference-free deconvolution methods such as STdeconvolve.