标题： scMamba：一种超越高度可变特征选择的单细胞多组学整合的可扩展基础模型 显示英文标题

标题： scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection

摘要： 单细胞多组学技术的出现使得在单个细胞内同时分析多种组学层面成为可能。 整合这种多模态数据为细胞身份、调控过程和疾病机制提供了前所未有的见解。 然而，这仍然具有挑战性，因为当前的方法通常依赖于在预处理过程中选择高变异基因或峰，这可能会无意中丢弃关键的生物学信息。 在此，我们提出了 scMamba，这是一种基础模型，旨在无需先验特征选择的情况下整合单细胞多组学数据，同时保留基因组位置信息。 scMamba 引入了一种基于块的细胞标记化策略，将基因组区域视为单词（标记），将细胞视为句子。 基于状态空间对偶性的概念，scMamba 从高维、稀疏的单细胞多组学数据中提炼出丰富的生物学见解。 此外，我们新颖的对比学习方法，结合余弦相似性正则化，相比传统方法在跨组学层面对齐方面表现出色。 在多个数据集上的系统基准测试表明，scMamba 在保持生物学变异、对齐组学层次以及增强关键下游任务（如聚类、细胞类型注释和轨迹推断）方面显著优于最先进的方法。 我们的研究结果使 scMamba 成为一个强大的工具，用于大规模单细胞多组学整合，能够处理大规模图谱并推动生物学发现。 显示英文摘要

摘要： The advent of single-cell multi-omics technologies has enabled the simultaneous profiling of diverse omics layers within individual cells. Integrating such multimodal data provides unprecedented insights into cellular identity, regulatory processes, and disease mechanisms. However, it remains challenging, as current methods often rely on selecting highly variable genes or peaks during preprocessing, which may inadvertently discard crucial biological information. Here, we present scMamba, a foundation model designed to integrate single-cell multi-omics data without the need for prior feature selection while preserving genomic positional information. scMamba introduces a patch-based cell tokenization strategy that treats genomics regions as words (tokens) and cells as sentences. Building upon the concept of state space duality, scMamba distills rich biological insights from high-dimensional, sparse single-cell multi-omics data. Additionally, our novel contrastive learning approach, enhanced with cosine similarity regularization, enables superior alignment across omics layers compared to traditional methods. Systematic benchmarking across multiple datasets demonstrates that scMamba significantly outperforms state-of-the-art methods in preserving biological variation, aligning omics layers, and enhancing key downstream tasks such as clustering, cell type annotation, and trajectory inference. Our findings position scMamba as a powerful tool for large-scale single-cell multi-omics integration, capable of handling large-scale atlases and advancing biological discovery.