Presented by Dr. Brendan Miller, Johns Hopkins University Recent technological advancements have enabled spatially resolved transcriptomic (ST) profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type spatial co-localization patterns. Supervised deconvolution approaches have recently been developed to predict the proportion of cell-types within ST multi-cellular pixels but these approaches rely on the availability of a suitable single-cell reference, which may present limitations if such a reference does not exist. To address this challenge, we developed STdeconvolve as an unsupervised approach that builds upon latent Dirichlet allocation to deconvolve underlying cell-types comprising such ST datasets. We show that STdeconvolve effectively recovers the putative transcriptomic profiles of cell-types and their proportional representation within ST multi-cellular pixels without reliance on external single-cell transcriptomics references. We find that STdeconvolve provides competitive performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available.