Methods, Tools and Algorithms in Single-Cell Bioinformatics

The Single Cell Bioinformatics section is dedicated to publishing research on the theoretical and computational foundations of single-cell genomics. This Topic explicitly seeks innovative tools, methods, and algorithms that advance the full lifecycle of single-cell data generation, processing, integration, analysis, and interpretation across genomics, transcriptomics, epigenomics, proteomics, and spatial assays.

We welcome submissions that deliver methodological innovation in three broad areas: (1) core analytics for quantification and preprocessing (demultiplexing, alignment, error correction, UMI-aware counting) and multimodal integration/batch correction (e.g., scRNA-seq with scATAC-seq; multi-omic and spatial integration); (2) discovery and modeling, including clustering, trajectory inference, lineage tracing, dynamic models of cell fate, gene regulatory network inference, chromatin accessibility modeling, and enhancer–promoter mapping, alongside robust differential and compositional analyses designed for rare-cell and perturbation studies; and (3) scalable computation and rigorous practice, spanning approximate inference, streaming algorithms, cloud/distributed frameworks, uncertainty quantification, benchmarking, reproducibility, and standards for datasets, annotations, and ontologies. We also encourage user-facing software that supports data sharing, visualization, interactive analysis, and FAIR, interoperable pipelines, as well as method-driven applications in development, immunology, oncology, neurobiology, microbiology, and clinical translation.

By foregrounding tools, methods, and algorithms, this Topic aims to accelerate discovery and enable robust, scalable, and interpretable single-cell and spatial analyses, with clear pathways to real-world impact. Submissions that emphasize transparent evaluation and community standards are strongly encouraged.