Work

PopSVG enables scalable detection of spatially variable genes in population-level spatial transcriptomics

Year: 2026

Type: article

Abstract: Spatially variable gene (SVG) detection is one of the most important tasks in spatial transcriptomics (ST) data analysis. To identify SVGs from population-level ST data, current practices often call SVGs separately from individual slices and then combine, or select highly variable genes from the concatenated expression matrix of all subjects as a surrogate. These approaches fail to simultaneously account for the common and subject-specific spatial patterns, leading to low accuracy and power. To overcome this issue, we develop PopSVG, a statistical method for population-level SVG detection. PopSVG hierarchically models the spatial expression from a population of biological replicates, balancing inter-subject homogeneity and heterogeneity. After parameter estimation, PopSVG computes statistical significance of genes being population-level SVGs. Extensive experiments demonstrate PopSVG’s superiority over existing approaches in identifying biologically relevant SVGs and improving multi-slice tissue domain segmentation, while scaling efficiently to large datasets. A Python implementation of PopSVG is available at https://github.com/ToryDeng/PopSVG. A scalable statistical method that detects spatially variable genes at the population level in spatial transcriptomics and characterizes their prevalence and spatial pattern strength across subjects.

Source: Communications Biology

DOI: 10.1038/s42003-026-10648-4

Authors: Tao Deng, Zhe Yu, Xiaobo Sun, Tianwei Yu, Hao Wu