Unsupervised spatially embedded deep representation of spatial transcriptomics.
Genome Med
; 16(1): 12, 2024 01 12.
Article
en En
| MEDLINE
| ID: mdl-38217035
ABSTRACT
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/ ).
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Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Comunicación Celular
/
Perfilación de la Expresión Génica
Límite:
Humans
Idioma:
En
Año:
2024
Tipo del documento:
Article