Deciphering tissue heterogeneity from spatially resolved transcriptomics by the autoencoder-assisted graph convolutional neural network

Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to investigate the complex and heterogeneous tissue organization. However, it is challenging for a single model to learn an effective representation within and across spatial contexts. To solve the issue, we develop a nov...

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Detalles Bibliográficos
Autores principales: Li, Xinxing, Huang, Wendong, Xu, Xuan, Zhang, Hong-Yu, Shi, Qianqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Genetics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248005/
https://www.ncbi.nlm.nih.gov/pubmed/37303949
http://dx.doi.org/10.3389/fgene.2023.1202409

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248005/
https://www.ncbi.nlm.nih.gov/pubmed/37303949
http://dx.doi.org/10.3389/fgene.2023.1202409

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