Enhanced spatially resolved transcriptomics analysis by matching between expression profiles and spatial topology

Spatially resolved transcriptomics (SRT) quantifies gene expression covering contextual heterogeneity for the tissue section. Exploratory data analysis using SRT data sheds light on diverse biomedical research fields. We propose STForte, a pairwise graph autoencoding-based approach for SRT data analysis, which is capable of matching the information between expression profiles and spatial topology in the latent space. STForte benefits from the designed framework to provide encodings with justifiable spatial correlations for the down-stream analysis of both homogeneous and heterogeneous SRT data. Moreover, STForte can unravel the biological patterns of unobserved locations or recover deficient measurements to enable spatial enhancement. Latent encodings generated by STForte can be used to perform spatial region identification or other downstream tasks to gain biological insights and elucidate biological processes. In this work, we presented various analyses to show that STForte is scalable for analyzing SRT data under different scenarios with considerable performance.