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,...
Enregistré dans:
Auteurs principaux: | Yasushi Okochi, Shunta Sakaguchi, Ken Nakae, Takefumi Kondo, Honda Naoki |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/abbd231b7f69497dae153503ce587d0f |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
ClusterMap for multi-scale clustering analysis of spatial gene expression
par: Yichun He, et autres
Publié: (2021) -
Theory of z-linear maps
par: Moreno Salguero, Yolanda
Publié: (2003) -
Spatial gene expression maps of the intestinal lymphoid follicle and associated epithelium identify zonated expression programs.
par: Noam Cohen, et autres
Publié: (2021) -
Linear mapping approximation of gene regulatory networks with stochastic dynamics
par: Zhixing Cao, et autres
Publié: (2018) -
Stochastic pulsing of gene expression enables the generation of spatial patterns in Bacillus subtilis biofilms
par: Eugene Nadezhdin, et autres
Publié: (2020)