SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration
Year: 2026
Type: article
Abstract: 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.
Source: Nature Communications
Institutions: Chinese University of Hong Kong, Mohamed bin Zayed University of Artificial Intelligence, University of Edinburgh, Huazhong University of Science and Technology, University of Hong Kong, Nanyang Technological University, Baruch College, City University of New York, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Guangzhou Laboratory, Guangzhou Medical University, King Abdullah University of Science and Technology, Chinese University of Hong Kong, Shenzhen
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
OpenAlex ID: W7159942228