Identification of spatial expression trends in single-cell gene expression data.
Nat Methods
; 15(5): 339-342, 2018 05.
Article
em En
| MEDLINE
| ID: mdl-29553578
ABSTRACT
As methods for measuring spatial gene expression at single-cell resolution become available, there is a need for computational analysis strategies. We present trendsceek, a method based on marked point processes that identifies genes with statistically significant spatial expression trends. trendsceek finds these genes in spatial transcriptomic and sequential fluorescence in situ hybridization data, and also reveals significant gene expression gradients and hot spots in low-dimensional projections of dissociated single-cell RNA-seq data.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Regulação da Expressão Gênica
/
Análise de Sequência de RNA
/
Análise de Célula Única
Tipo de estudo:
Diagnostic_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article