Bridging unpaired single-cell multimodal data for integrative analyses with SuperMap

• 成果类型: Article
• 署名作者: Deng, Chao; Ma, Xinyi; Lu, Hui; Zhao, Hongyu; Ming, Jingsi; Wang, Tao
• 署名单位: Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Yale University; East China Normal University; East China Normal University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2505182123
• 发表日期: 2026-02-10
• 页码: e2505182123
• 关键词: cross-modality data integration; gene activity score; regression with unlinked data; chromatin; omics

摘要

Current single-cell profiling technologies enable the capture of multiple cellular modalities, providing valuable insights into complex biological systems. While a substantial amount of single-cell multimodal data has been generated and accumulated, most of these datasets are unpaired, characterized by distinct feature spaces and a lack of cell-wise correspondence. The absence of explicit linkages between modalities poses a fundamental challenge for data integration and interpretation. To address this, we introduce SuperMap, a statistical learning method designed for the integrative analyses of unpaired multimodal data. SuperMap directly learns cross-modal mappings from unpaired data to effectively bridge and link different modalities, facilitating a variety of downstream analysis tasks. Comprehensive benchmarking and real-world applications demonstrate the superior performance of SuperMap in enhancing cell-type identification, improving diagonal integration, enabling regulatory analysis, and revealing epigenomic priming events to specify cell differentiation directions for trajectory inference.