Abstract
Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmarked Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics datasets and platforms and demonstrated the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Applications to a human pancreatic cancer dataset revealed cancer clone-specific T cell infiltration, and application to lymph node samples identified subtle cellular surroundings between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.
Competing Interest Statement
The authors have declared no competing interest.
