Statistical Identification of Cell Type-Specific Spatially Variable Genes in Spatial Transcriptomics

Authors

Lulu Shang
Peijun Wu
Xiang Zhou

Publication Date

1-26-2025

Journal

Nature Communications

Abstract

An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)-a subset of SVGs exhibiting distinct spatial expression patterns within specific cell types. Celina utilizes a spatially varying coefficient model to accurately capture each gene's spatial expression pattern in relation to the distribution of cell types across tissue locations, ensuring effective type I error control and high power. Celina proves powerful compared to existing methods in single-cell resolution spatial transcriptomics and stands as the only effective solution for spot-resolution spatial transcriptomics. Applied to five real datasets, Celina uncovers ct-SVGs associated with tumor progression and patient survival in lung cancer, identifies metagenes with unique spatial patterns linked to cell proliferation and immune response in kidney cancer, and detects genes preferentially expressed near amyloid-β plaques in an Alzheimer's model.

Keywords

Humans, Transcriptome, Lung Neoplasms, Gene Expression Profiling, Alzheimer Disease, Kidney Neoplasms, Single-Cell Analysis, Plaque, Amyloid

DOI

10.1038/s41467-025-56280-4

PMID

39865128

PMCID

PMC11770176

PubMedCentral® Posted Date

1-26-2025

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes