Based on preserving spatial location and histological images,the spatial transcriptome can provide new insights into tissue structure and organism development with the data of gene expression profile.To precisely identify the spatial domain of loci is an important step in various downstream analyses of spatial transcriptome.The proposed EnST algorithm adds a variogram self-encoder based on the use of composite scaling networks,which can extract useful information in spatial transcriptome data.In the spatial transcriptome dataset of human breast cancer,the EnST algorithm can made a better description on the fine spatial organization structure of breast cancer in comparison with other seven algorithms.In addition,the representations learned by EnST showed a powerful performance on the downstream tasks of clustering,visualization,differential gene expression analysis and GO function analysis.
deep learningvariational graph autoencoderbreast cancerspatial domainclustering
NETL
NSTL
万方数据
