scMultiMap: Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data

Chang Su (), Dongsoo Lee, Peng Jin and Jingfei Zhang ()
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Chang Su: Emory University
Dongsoo Lee: Emory University
Peng Jin: Emory University
Jingfei Zhang: Emory University

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Mapping enhancers and target genes in disease-related cell types provides critical insights into the functional mechanisms of genome-wide association studies (GWAS) variants. Single-cell multimodal data, which measure gene expression and chromatin accessibility in the same cells, enable the cell-type-specific inference of enhancer-gene pairs. However, this task is challenged by high data sparsity, sequencing depth variation, and the computational burden of analyzing a large number of pairs. We introduce scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derived p-values. In blood and brain data, scMultiMap shows appropriate type I error control, high statistical power, and computational efficiency (1% of existing methods). When applied to Alzheimer’s disease (AD) data, scMultiMap gives the highest heritability enrichment in microglia and reveals insights into the regulatory mechanisms of AD GWAS variants.

Date: 2025
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DOI: 10.1038/s41467-025-59306-z

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