Unsupervised spatially embedded deep representation of spatial transcriptomics

Unsupervised spatially embedded deep representation of spatial transcriptomics

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-s...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Hang Xu, Huazhu Fu, Yahui Long, Kok Siong Ang, Raman Sethi, Kelvin Chong, Mengwei Li, Rom Uddamvathanak, Hong Kai Lee, Jingjing Ling, Ao Chen, Ling Shao, Longqi Liu, Jinmiao Chen
Format: Artykuł
Język:English
Wydane: BMC 2024-01-01
Seria:Genome Medicine
Hasła przedmiotowe:
Spatial transcriptomics
Spatial clustering
Variational graph auto-encoder
Batch integration
Trajectory inference
Gene imputation
Dostęp online:https://doi.org/10.1186/s13073-024-01283-x

Internet

https://doi.org/10.1186/s13073-024-01283-x

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