Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding.
Nat Commun
; 13(1): 7640, 2022 12 10.
Article
en En
| MEDLINE
| ID: mdl-36496406
ABSTRACT
Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here we propose Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of reference single-cell RNA-seq data and the spatial information of spatially resolved transcriptomics data. We present a series of benchmarking analyses on publicly available spatially resolved transcriptomics datasets, that demonstrate the superiority of Spatial-ID compared with state-of-the-art methods. Besides, we apply Spatial-ID on a self-collected mouse brain hemisphere dataset measured by Stereo-seq, that shows the scalability of Spatial-ID to three-dimensional large field tissues with subcellular spatial resolution.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Perfilación de la Expresión Génica
/
Análisis de la Célula Individual
Límite:
Animals
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2022
Tipo del documento:
Article
País de afiliación:
China