Model-based prediction of spatial gene expression via generative linear mapping
Single cell RNA-seq loses spatial information of gene expression in multicellular systems because tissue must be dissociated. Here, the authors show the spatial gene expression profiles can be both accurately and robustly reconstructed by a new computational method using a generative linear mapping,...
Guardado en:
Autores principales: | Yasushi Okochi, Shunta Sakaguchi, Ken Nakae, Takefumi Kondo, Honda Naoki |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/abbd231b7f69497dae153503ce587d0f |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
ClusterMap for multi-scale clustering analysis of spatial gene expression
por: Yichun He, et al.
Publicado: (2021) -
Theory of z-linear maps
por: Moreno Salguero, Yolanda
Publicado: (2003) -
Spatial gene expression maps of the intestinal lymphoid follicle and associated epithelium identify zonated expression programs.
por: Noam Cohen, et al.
Publicado: (2021) -
Linear mapping approximation of gene regulatory networks with stochastic dynamics
por: Zhixing Cao, et al.
Publicado: (2018) -
Stochastic pulsing of gene expression enables the generation of spatial patterns in Bacillus subtilis biofilms
por: Eugene Nadezhdin, et al.
Publicado: (2020)