SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains

Rui Jiang; Zhen Li; Yuhang Jia; Siyu Li; Shengquan Chen

doi:10.3390/cells12040604

SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains

Cells. 2023 Feb 13;12(4):604. doi: 10.3390/cells12040604.

Authors

Rui Jiang¹, Zhen Li¹, Yuhang Jia², Siyu Li², Shengquan Chen³

Affiliations

¹ MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST/Department of Automation, Tsinghua University, Beijing 100084, China.
² School of Statistics and Data Science, Nankai University, Tianjin 300071, China.
³ School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.

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.

Keywords: spatial autocorrelation; spatial domains; spatial transcriptomics; spatially variable genes.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Gene Expression Profiling*
Transcriptome*

Grants and funding

This research was funded by the National Key Research and Development Program of China, grant number 2021YFF1200902, and the National Natural Science Foundation of China, grant numbers 62203236, 62273194, 61873141 and 61721003.