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Abstract:
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Spatial transcriptomics technologies have enabled gene expression profiling on complex tissues with spatial localization information. The majority of these technologies, however, effectively measure on each tissue location the average gene expression from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, CARD, that leverages cell type specific expression information from single cell RNA sequencing for the deconvolution of spatial transcriptomics. A unique feature of CARD is its ability to model the spatial correlation in cell type composition across tissue locations, thus enabling spatially informed cell type deconvolution. Modeling spatial correlation allows us to borrow the cell type composition information across locations on the entire tissue to accurately infer the cell type composition on each individual location, achieve robust deconvolution performance in the presence of mismatched scRNA-seq reference, impute cell type compositions and gene expression levels on unmeasured tissue locations, and facilitate the construction of a refined spatial tissue map with a resolution much higher than the original.
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