Scholarlink

<p>Recent advancements in single-cell sequencing technologies have led to the generation of vast amounts of multi-omics data, spurring the development of numerous integration tools. While multi-omics integration has significantly advanced cell research, there is still a lack of comprehensive evaluations and guidelines for these tools. This study benchmarks Domain-specific Models (DMs) and Foundation Models (FMs) for multi-omics data integration, assessing 23 methods with optimized hyperparameters on integration accuracy, biomarker detection, trajectory inference, and quantitative batch effect correction. We address current gaps in assessing the efficacy and limitations of FMs compared to DMs in the multi-omics integration task. Importantly, our comprehensive analysis goes beyond basic integration accuracy, focusing on the preservation of cellular characteristics, transcriptomic biomarkers, epigenomic regulatory elements, and development trajectories. This holistic approach enables researchers to extract meaningful insights from integration results, facilitating a deeper understanding of individual cells. Generally, we find FMs fall short of state-of-the-art DMs in this field. To bridge this performance gap, we propose a lightweight adaptation strategy that enhances their effectiveness in this task. Our findings serve as a guide for researchers in selecting suitable integration methods for specific single-cell analysis objectives and provide insights for future model design.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月2日

查看全文
  • Yixuan Wang,
  • Yimin Fan,
  • Xuesong Wang,
  • Tingyang Yu,
  • Yongshuo Zong,
  • Xinyuan Liu,
  • Gaoyang Zhong,
  • Meitong Liu,
  • Qing Li,
  • Kin Hei Lee,
  • Khachatur Dallakyan,
  • Zhichao Hu,
  • Yaqian Qi,
  • Junjie Huang,
  • Gengjie Jia,
  • Jiao Yuan,
  • Ting-Fung Chan,
  • Xin Gao,
  • Irwin King,
  • Yu Li

摘要

Recent advancements in single-cell sequencing technologies have led to the generation of vast amounts of multi-omics data, spurring the development of numerous integration tools. While multi-omics integration has significantly advanced cell research, there is still a lack of comprehensive evaluations and guidelines for these tools. This study benchmarks Domain-specific Models (DMs) and Foundation Models (FMs) for multi-omics data integration, assessing 23 methods with optimized hyperparameters on integration accuracy, biomarker detection, trajectory inference, and quantitative batch effect correction. We address current gaps in assessing the efficacy and limitations of FMs compared to DMs in the multi-omics integration task. Importantly, our comprehensive analysis goes beyond basic integration accuracy, focusing on the preservation of cellular characteristics, transcriptomic biomarkers, epigenomic regulatory elements, and development trajectories. This holistic approach enables researchers to extract meaningful insights from integration results, facilitating a deeper understanding of individual cells. Generally, we find FMs fall short of state-of-the-art DMs in this field. To bridge this performance gap, we propose a lightweight adaptation strategy that enhances their effectiveness in this task. Our findings serve as a guide for researchers in selecting suitable integration methods for specific single-cell analysis objectives and provide insights for future model design.