Cargando…
Inferring spatial and signaling relationships between cells from single cell transcriptomic data
Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively sma...
Autores principales: | Cang, Zixuan, Nie, Qing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190659/ https://www.ncbi.nlm.nih.gov/pubmed/32350282 http://dx.doi.org/10.1038/s41467-020-15968-5 |
Ejemplares similares
-
DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data
por: Maseda, Floyd, et al.
Publicado: (2021) -
Deciphering tissue structure and function using spatial transcriptomics
por: Walker, Benjamin L., et al.
Publicado: (2022) -
Screening cell–cell communication in spatial transcriptomics via collective optimal transport
por: Cang, Zixuan, et al.
Publicado: (2023) -
CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data
por: Bae, Sungwoo, et al.
Publicado: (2022) -
Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data
por: Sha, Yutong, et al.
Publicado: (2020)