Cargando…
Revealing Tissue Heterogeneity and Spatial Dark Genes from Spatially Resolved Transcriptomics by Multiview Graph Networks
Spatially resolved transcriptomics (SRT) is capable of comprehensively characterizing gene expression patterns and providing an unbiased image of spatial composition. To fully understand the organizational complexity and tumor immune escape mechanism, we propose stMGATF, a multiview graph attention...
Autores principales: | Li, Ying, Lu, Yuejing, Kang, Chen, Li, Peiluan, Chen, Luonan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511271/ https://www.ncbi.nlm.nih.gov/pubmed/37736108 http://dx.doi.org/10.34133/research.0228 |
Ejemplares similares
-
Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning
por: Zuo, Chunman, et al.
Publicado: (2022) -
Deciphering tissue heterogeneity from spatially resolved transcriptomics by the autoencoder-assisted graph convolutional neural network
por: Li, Xinxing, et al.
Publicado: (2023) -
Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion
por: Li, Zhuliu, et al.
Publicado: (2021) -
Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder
por: Dong, Kangning, et al.
Publicado: (2022) -
SpatialDB: a database for spatially resolved transcriptomes
por: Fan, Zhen, et al.
Publicado: (2020)