Insights in Single Cell Bioinformatics

Single-cell technologies let us study biology one cell at a time, transforming our view of tissues, development and disease. The Single Cell Bioinformatics section sits where statistics, machine learning, genomics and cell biology meet, turning sparse and high-dimensional measurements into a map of cellular states. This collection celebrates that progress: it brings together leading voices to highlight state-of-the-art developments and to set the agenda for the questions that will define the field next.This Research Topic aims to provide an overview of the most recent advances in single-cell bioinformatics. It seeks to show how clustering, trajectory inference, integration and spatial analysis can resolve cellular heterogeneity and link cells to function. Without losing sight of past achievements, the goal is to explore the potential of future work, addressing the scalability, batch-effect and data challenges that remain at the forefront of this multidisciplinary area.The scope is broadly defined yet focused on areas where significant innovative strides have been made. We welcome contributions that emphasize:• Single-cell RNA, ATAC and multi-omics analysis• Clustering, annotation and cell-type identification• Trajectory inference and RNA velocity• Data integration, batch correction and atlas building• Spatial transcriptomics and tissue mapping• Deep learning and foundation models for single-cell data• Open, reproducible tools and benchmarks for single-cell analysisThis collection welcomes contributions reflecting on current developments and plotting pathways for upcoming research, from Editorial Board Members of the section, from authors they refer, and, on invitation, from a small number of external researchers whose work is shaping the direction of the field. Authors are encouraged to engage critically with their fields, identify the current challenges and propose novel solutions that advance bioinformatics tools and their application to real biological problems. Topic editors: Virginia Savova, Dayu Hu, Luis Rueda Source: https://www.frontiersin.org/research-topics/84026/insights-in-single-cell-bioinformatics