Unsupervised spatially embedded deep representation of spatial transcriptomics

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 higher clustering performance on manually annotated 10  × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR’s ability to impute and denoise gene expression (URL:https://github.com/JinmiaoChenLab/SEDR/).
Source: Genome Medicine - Category: Genetics & Stem Cells Source Type: research