SEDIST: Spatially Enhanced Domain Identification through Spatial Transcriptomics

Motivation: The recent advancement of spatially resolved transcriptomics provides a way to investigate cellular heterogeneity and tissue micro-environment using gene expression profiles with spatial context, often accommodated with histological data. However, precise spatial domain identification, multisample integration, and cellular decomposition still remain challenging. Results Here we introduce SEDIST, a novel method that integrates autoencoders and graph neural networks with self-supervised contrastive learning to utilize spatial transcriptomics data effectively. This approach enhances the model’s ability to extract informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots through self-supervised contrastive learning. Highly expressed genes in different domain layers are also identified and 3D embedded plot of clusters along with diffusion map are also graphically represented as downstream analysis. This comprehensive integration of spatial context leads to superior performance compared to existing state-of-the-art (SOTA) methods in the human brain dorsolateral prefrontal cortex (DLPFC) and the BRCA (human breast cancer) 10X Visium dataset, as evidenced by achieving an Adjusted Rand Index (ARI) of 0.6466 and 0.6185, respectively.