Year: 2026
Type: preprint
Abstract: Motivation Many spatial domain identification methods describe the neighbourhood of a cell by mean gene expression. However, the mean cannot separate neighbourhoods that differ only in gene–gene covariance and tissue is organised at several spatial scales.ht Results We propose CoSMIM, which represents the niche of each cell by a weighted fusion of gene–gene covariance matrices of its spatial neighbourhood at multiple scales. Principal component analysis and Leiden clustering of the matrix square root of the fused matrix identify spatial domains without model training. On six benchmark datasets from five platforms, CoSMIM attains the best overall rank over eight evaluation metrics among 16 methods. It also ranks first on a Xenium mouse brain dataset, and on all seven datasets its median NMI exceeds that of single-scale COVET. The domain covariance matrices also show gene pairs whose correlation differs between domains. Availability and implementation The source code, documentation and test data of CoSMIM are available at GitHub ( https://github.com/barondu/cosmim ). Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.
Authors: Bairui Du, Hisanori Kiryu
Institution: The University of Tokyo
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7223571964