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40832731
SCRIPT: Predicting Single-Cell Long-Range Cis-Regulation Based on Pretrained Graph Attention Networks.

Adv Sci (Weinh), 2025/11;12(41):e05021.

Zhang Y[1, 2, 3], Wen B[4], Jiao Y[2], Liu Y[1, 2], Guo X[2], Wu Y[2], Li J[2], Han L[1, 2], Xu Y[1, 3], Gao X[2, 5, 6, 7], Qi Y[1, 2, 8], Cheng Y[1, 2], He Y[2], Tian W[4, 9, 10]

Affiliations

PMID: 40832731DOI: 10.1002/advs.202505021

Impact factor: 17.521

Abstract
Single-cell cis-regulatory relationships (CRRs) are essential for deciphering transcriptional regulation and understanding the pathogenic mechanisms of disease-associated non-coding variants. Existing computational methods struggle to accurately predict single-cell CRRs due to inadequately integrating causal biological principles and large-scale single-cell data. Here, SCRIPT (Single-cell Cis-regulatory Relationship Identifier based on Pre-Trained graph attention networks) is presented for inferring single-cell CRRs from transcriptomic and chromatin accessibility data. SCRIPT incorporates two key innovations: graph causal attention networks supported by empirical CRR evidence, and representation learning enhanced through pretraining on atlas-scale single-cell chromatin accessibility data. Validation using cell-type-specific chromatin contact and CRISPR perturbation data demonstrates that SCRIPT achieves a mean AUC of 0.89, significantly outperforming state-of-the-art methods (AUC: 0.7). Notably, SCRIPT obtains an over twofold improvement in predicting long-range CRRs (>100 Kb) compared to existing methods. By applying SCRIPT to Alzheimer's disease and schizophrenia, a framework is established for prioritizing disease-causing variants and elucidating their functional effects in a cell-type-specific manner. By uncovering molecular genetic mechanisms undetected by existing computational methods, SCRIPT provides a roadmap for advancing genetic diagnosis and target discovery.

Keywords: cis‐regulation; graph neural networks; non‐coding variant; pretrain; single cell

MeSH terms
Single-Cell Analysis; Humans; Computational Biology; Chromatin; Gene Expression Regulation
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