SNMF: Ultrafast, Spatially-Aware Deconvolution for Spatial Transcriptomics

Sequencing-based spatial transcriptomics has revolutionized the study of tissue architecture, but its ‘spots’ often contain multiple cells, creating a key computational challenge, termed deconvolution, to decipher each spot’s cell-type composition. Reference-free deconvolution methods avoid the need for a matched single-cell RNA-seq dataset, but typically neglect the spatial correlation between neighboring spots and do not leverage modern hardware for efficient computation. Here, we propose SNMF (Spatial Non-negative Matrix Factorization): a rapid, accurate, and reference-free deconvolution method. SNMF extends the standard NMF framework with a spatial mixing matrix that models neighborhood influences, guiding the factorization toward spatially coherent solutions. Our R package is, to our knowledge, the first spatial transcriptomics deconvolution tool to natively support GPU execution, completing benchmark analyses in under one minute—over two orders of magnitude faster than the slowest competing methods— with moderate memory requirements. On synthetic and real benchmark datasets, SNMF significantly outperforms state-of-the-art methods in deconvolution accuracy, and on a human melanoma dataset it recovers biologically meaningful cell-type signatures—including a tumor-boundary transition zone— without any reference input. The proposed mehtod is publicly available at https://github.com/ML4BM-Lab/SNMF .