Journal Article

Unsupervised spatially embedded deep representation of spatial transcriptomics

Chen, Ao, Fu, Huazhu, Uddamvathanak, Rom, Liu, Longqi, Ling, Jingjing, Chong, Kelvin, Lee, Hong Kai, Ang, Kok Siong, Shao, Ling, Sethi, Raman, Chen, Jinmiao, Xu, Hang, Long, Yahui, Li, Mengwei

2024

Overview

Optimal integration of transcriptomics data and associated spatial information is essential 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 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/ ).

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Anopheles / Applications of technology in health and disease / B cells / Batch integration / Bioinformatics / Biomedical and Life Sciences / Biomedicine / Cancer Research / Clustering / Gene expression / Gene imputation / Genes / Genomics / Geospatial data / Human Genetics / Learning / Medicine/Public Health / Metabolomics / Method / Neural networks / Sparsity / Spatial clustering / Spatial transcriptomics / Systems Biology / Trajectory inference / Transcriptomics / Variational graph auto-encoder