Advances in Single-Cell Multi-Omics Data Analysis

Dear Colleagues,

Single-cell multi-omics is transforming our ability to understand cellular identity, state, and function in complex biological systems. While early single-cell studies focused primarily on transcriptomic profiling, recent advances now enable the simultaneous measurement of multiple molecular layers within individual cells, including chromatin accessibility, DNA methylation, protein abundance, spatial context, and other regulatory features. These technologies are providing an increasingly comprehensive view of cellular heterogeneity, lineage relationships, and molecular regulation in development, physiology, and disease.

This Collection aims to showcase advances in the computational and statistical analysis of single-cell multi-omics data, with a particular emphasis on methods and applications that enable deeper biological insight from increasingly complex and high-dimensional datasets. We welcome studies that develop or apply innovative approaches for data integration, modality alignment, batch correction, cell-type annotation, trajectory inference, regulatory network reconstruction, and interpretable modeling across diverse single-cell platforms.

Recent breakthroughs in machine learning, deep learning, and probabilistic modeling are accelerating the field, while new benchmarking efforts and software tools are improving robustness, reproducibility, and scalability. At the same time, the rapid expansion of spatially resolved and clinically relevant single-cell multi-omics datasets is opening new opportunities to study tissue organization, disease progression, immune responses, and therapeutic mechanisms at unprecedented resolution.

We invite original research, reviews, and perspectives covering, but not limited to, the following:

• Methods for integrating and harmonizing multi-modal single-cell datasets across platforms, tissues, and conditions;
• Computational approaches for clustering, visualization, cell-type annotation, and identification of rare or transitional cell states;
• Trajectory inference, lineage reconstruction, and modeling of dynamic cellular processes using multi-omics data;
• Gene regulatory network analysis, cross-modal interactions, and mechanistic interpretation of cell-state transitions;
• Machine learning, deep learning, and interpretable artificial intelligence for single-cell multi-omics analysis;
• Benchmarking, validation, reproducibility, and software frameworks for robust multi-omics data analysis;
• Spatial and spatiotemporal single-cell multi-omics methods and applications;
• Applications of single-cell multi-omics in cancer, immunology, neuroscience, developmental biology, and precision medicine.

By bringing together work from bioinformatics, computational biology, statistics, and biomedical research, this Collection seeks to advance the analytical foundations of single-cell multi-omics and to accelerate its impact on biological discovery and translational science.

Dr. Wenji Ma
Dr. Zongjie Wang
Guest Editors

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