SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics.

ABSTRACT: Recent advancements in spatial transcriptomic technologies have enabled the measurement of whole transcriptome profiles with preserved spatial context. However, limited by spatial resolution, the measured expressions at each spot are often from a mixture of multiple cells. Computational deconvolution methods designed for spatial transcriptomic data rarely make use of the valuable spatial information as well as the neighboring similarity information. Here, we propose SONAR, a Spatially weighted pOissoN-gAmma Regression model for cell-type deconvolution with spatial transcriptomic data. SONAR directly models the raw counts of spatial transcriptomic data and applies a geographically weighted regression framework that incorporates neighboring information to enhance local estimation of regional

SUBMITTER: Liu Z 

PROVIDER: S-EPMC10406862 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature