Investigating Spatial Dynamics in Spatial Omics Data with StarTrail

• 成果类型: Article; Early Access
• 署名作者: Chen, Jiawen; Xiong, Caiwei; Sun, Quan; Song, Yutong; Wang, Geoffery W.; Gupta, Gaorav P.; Halder, Aritra; Li, Yun; Li, Didong
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of Pennsylvania; Pennsylvania Medicine; Childrens Hospital of Philadelphia; University of Pennsylvania; Pennsylvania Medicine; Childrens Hospital of Philadelphia; University of Pennsylvania; Pennsylvania Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Drexel University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2654225
• 发表日期: 2026-06-09
• 关键词: Boundary detection; Cliff gene; Gaussian process; gradients; gene-expression

摘要

Spatial omics technologies revolutionize our view of biological processes within tissues. However, existing methods fail to capture localized, sharp changes characteristic of critical events (e.g., tumor development). Here, we present StarTrail, a novel gradient based method that powerfully defines rapidly changing regions and detects cliff genes, genes exhibiting drastic expression changes at highly localized or disjoint boundaries. StarTrail, the first to leverage spatial gradients for spatial omics data, also quantifies directional dynamics. Across multiple datasets, StarTrail accurately delineates boundaries (e.g., brain layers, tumor-immune boundaries), and detects cliff genes that may regulate molecular crosstalk at these biologically relevant boundaries but are missed by existing methods. StarTrail, filling important gaps in current literature, enables deeper insights into tissue spatial architecture. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.