SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

The rapid development of spatial transcriptomics (ST) technol- demonstrated superior performance compared with both referenceogies has enabled transcriptome-wide profiling of gene expression based and reference-free approaches. Additionally, a pan-cancer in tissue sections. Despite the emergence of single-cell resolution clustering analysis on tumor spots identified by SpatialDeX unveiled platforms, most ST sequencing studies still operate at a multicell distinct tumor progression mechanisms both within and across resolution. Consequently, deconvolution of cell identities within the diverse cancer types. Overall, SpatialDeX is a valuable tool for spatial spots has become imperative for characterizing cell-type– unraveling the spatial cellular organization of tissues from ST data specific spatial organization. To this end, we developed Spatial without requiring single-cell RNA-seq references. Deconvolution Explorer (SpatialDeX), a regression model–based method for estimating cell-type proportions in tumor ST spots. Significance: The development of a reference-free method for SpatialDeX exhibited comparable performance to reference-based deconvolving the identity of cells in spatial transcriptomics methods and outperformed other reference-free methods with datasets enables exploration of tumor architecture to gain deeper simulated ST data. Using experimental ST data, SpatialDeX insights into the dynamics of the tumor microenvironment.