SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains.
Cells
; 12(4)2023 02 13.
Article
in En
| MEDLINE
| ID: mdl-36831270
ABSTRACT
Recent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadequate ability to decipher spatial domains, poor efficiency, and insufficient interoperability with existing standard analysis workflows still impede the applications of these methods. Here we propose SINFONIA, a scalable method for identifying spatially variable genes via ensemble strategies. Implemented in Python, SINFONIA can be seamlessly integrated into existing analysis workflows. Using 15 spatial transcriptomic datasets generated with different protocols and with different sizes, dimensions and qualities, we show the advantage of SINFONIA over three baseline methods and two variants via systematic evaluation of spatial clustering, domain resolution, latent representation, spatial visualization, and computational efficiency with 21 quantitative metrics. Additionally, SINFONIA is robust relative to the choice of the number of SVGs. We anticipate SINFONIA will facilitate the analysis of spatial transcriptomics.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Gene Expression Profiling
/
Transcriptome
Type of study:
Diagnostic_studies
/
Prognostic_studies
Language:
En
Journal:
Cells
Year:
2023
Document type:
Article