Denoising spatial epigenomic data via deep matrix factorization

Spatial epigenomics (SE) technologies profile epigenomic landscapes within intact tissues, preserving spatial context and enabling the study of gene regulatory mechanisms in situ. However, current SE datasets typically suffer from low signal detection, substantial noise and extremely sparse peak matrices, which pose considerable challenges for downstream analysis. Here we introduce SPEED (spatial epigenomic data denoising), a deep matrix factorization framework that leverages atlas-level single-cell epigenomic data and spatial context to impute and denoise SE data. In comprehensive benchmarks on both simulated data and real SE tissue datasets, SPEED outperformed five state-of-the-art methods across diverse tissues and technologies. Moreover, SPEED's denoised outputs facilitated downstream analyses such as differential chromatin accessibility analysis, epigenomic spatial domain identification and gene activity inference. Collectively, our results indicate that SPEED is a generalizable tool for improving data quality and biological insights in SE.