Xiaoyu Song1, Yuqing Shang1, Michelle E. Ehrlich2, Panos Roussos3,4, Guo-Cheng Yuan5 and Pei Wang6 1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857; 2Departments of Neurology, Pediatrics, and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029-5674, USA; 3Center for Disease Neurogenomics, Department of Psychiatry, Department of Genetics and Genomic Sciences, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029-5674, USA; 4Mental Illness Research Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, New York 10468, USA; 5Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029-5674, USA; 6Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029-5674, USA Corresponding author: song.xiaoyuduke-nus.edu.sg Abstract

Recent advances in spatially resolved transcriptomics (SRT) have provided valuable avenues for identifying cell–cell interactions and their critical roles in diseases. Here, we introduce QuadST, a novel statistical method for the robust and powerful identification of cell–cell interactions and their impacted genes in single-cell SRT. QuadST models interactions at different cell–cell distance quantile levels and innovatively contrasts signals to identify interaction-changed genes, which exhibit stronger signals at shorter distances. Unlike other methods, QuadST does not require the specification of interacting cell pairs. It is also robust against unmeasured confounding factors and measurement errors of the data. Simulation studies demonstrate that QuadST effectively controls the type I error, even in misspecified settings, and significantly improves power over existing methods. Applications of QuadST to real data sets reveal biologically significant interaction-changed genes across various cell types.

Footnotes

[Supplemental material is available for this article.]

Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.279859.124.

Freely available online through the Genome Research Open Access option.

Received August 2, 2024. Accepted June 13, 2025.