57: SpaDiff: Denoising for Sequence-based Spatial Transcriptomics via Diffusion Process

Music City Center 

Spatial transcriptomics is revolutionizing our understanding of complex biological systems by enabling the analysis of RNA transcriptomes with precise spatial resolution. The sequence-based spatial transcriptomics technology, such as Visium from 10X Genomics, provides critical insights into tissue architecture and cellular interactions within their native microenvironments. However, a significant challenge in spatial transcriptomics is the phenomenon of spot-swapping, where RNA molecules are not confined to their original locations on the tissue slide, introducing noise and inaccuracies into the data. To solve this problem, we propose SpaDiff which models spot-swapping via a diffusion process model. By applying Langevin MCMC, our model emulates the RNA molecules' diffusion and reverse diffusion processes, offering a more effective and generalizable solution to data denoising in spatial transcriptomics. By applying SpaDiff to multiple synthetic and real datasets, we show that it can not only retain the original UMI counts but also enhance the spatial specificity of biomarker gene expression, thereby improving the accuracy of subsequent analyses and the interpretation of biological p

Sequence-based Spatial Transcriptomics

Data Denoising

Diffusion Process

Score Function

Langevin MCMC 

Section on Statistics in Genomics and Genetics