towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out
inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled
with a masked self-supervised learning mechanism to construct a low-dimensional latent
representation of gene expression, which is then simultaneously embedded with the
corresponding spatial information through a variational graph autoencoder. SEDR achieved …