A novel spatially informed reference-free deconvolution method for spatial transcriptomics

Music City Center 

Cell-type deconvolution methods has been a driving force for rapid development of spatial transcriptomics (ST) technologies in the past few years. Though reference-based deconvolution methods have been extensively studied, there is still a large demand for methodology development with reference-free deconvolution. STdeconvolve is one of the earliest ref-free deconvolution methods. However, it does not take spatial information into account, limiting its practical utility. Here we introduce a reference-free approach called SpatialDC for spatially informed cell-type deconvolution for ST. In our model, we encourage spatially close spots share similar cell types, leading to improved spatial deconvolution results. We evaluate our model on both simulated and real datasets generated from various ST technologies, including manually annotated dataset (MOB), 10X Visium, and DBiT-seq. The SpatialDC framework demonstrates robust performance in recovering accurate cell-type proportions and transcriptional profiles while effectively accounting for spatial correlations between pixels. This work presents statistical and computational advancements for analyzing complex spatial gene expression data.

Spatial transcriptomics

Deconvolution

Reference-free

Latent Dirichlet Allocation (LDA) 

Section on Statistics in Genomics and Genetics