nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes.
Nat Commun
; 14(1): 4059, 2023 07 10.
Article
em En
| MEDLINE
| ID: mdl-37429865
ABSTRACT
Feature selection to identify spatially variable genes or other biologically informative genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available at https//bioconductor.org/packages/nnSVG .
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
/
Perfilação da Expressão Gênica
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article