Advances in Quantifying and Communicating Uncertainty in Single-Cell Biology

Single-cell omics is a transformative area within life sciences, enabling researchers to untangle cellular heterogeneity, uncover developmental lineages, and probe molecular states with unrivaled resolution. Despite remarkable advancements, challenges persist—traditional analytic pipelines often produce overly certain predictions, such as sharp cluster assignments or singular developmental trajectories, that do not fully reflect the ambiguities arising from data sparsity, measurement variability, batch effects, and reference limitations. These gaps in representing uncertainty can compromise the biological validity of findings, hinder result reproducibility, and slow the widespread adoption of single-cell technologies. Recent efforts have increasingly focused on acknowledging, quantifying, and communicating sources of uncertainty, applying statistical, probabilistic, and machine learning approaches to address these issues. Nevertheless, more advances are needed, especially as new platforms generate complex multimodal and spatially resolved datasets, and as questions about lineage, cellular neighborhoods, and inter-cell variation demand nuanced inference.This Research Topic aims to drive methodological breakthroughs and practical applications that rigorously quantify, propagate, calibrate, and interpret uncertainty throughout the single-cell analysis lifecycle. Our goal is to encourage the development and dissemination of frameworks, tools, and best practices that transparently communicate confidence, empower responsible biological discovery, and foster cross-study reproducibility. We are especially interested in both foundational innovations and applied studies that address uncertainty at all levels of single-cell analysis, as well as those bringing uncertainty quantification approaches to new domains such as multiomics, spatial transcriptomics, cell–cell communication, and large-scale, population-level studies. By bringing together contributions from statistics, machine learning, computational biology, and experimental science, we seek to advance community standards and cultivate robust, interpretable single-cell research.To gather further insights in transparency, reliability, and interpretability of uncertainty-aware single-cell analysis and its applications, this Research Topic invites contributions that span established and emerging approaches, model systems, and data modalities. Submissions may include (but are not limited to):- Models and methods to distinguish technical from biological uncertainty in single-cell data- Frameworks for uncertainty propagation in downstream analyses: differential expression, trajectory inference, pathway analysis, cell–cell communication, and cell fate mapping- Benchmarks, metrics, and best practices for evaluating and calibrating uncertainty-aware methodologies- Approaches for interpretability and dynamic visualization of uncertainty for diverse end users- Probabilistic and deep learning frameworks explicitly designed for large-scale, multimodal, or spatially resolved single-cell data- Strategies for uncertainty quantification in integrative multiomic, multi-resolution, or cross-platform analyses- Applications of uncertainty-aware methods to clinical and translational single-cell studies- Tools supporting uncertainty-based quality control, decision-making, and experimental design in single-cell research- Community resources, data repositories, annotated datasets, and reproducible pipelines facilitating uncertainty-aware analysisWe welcome original research articles, methods, reviews, perspectives, benchmark studies, case studies, and resource papers. Topic editors: Sheida Nabavi, Abhijeet R Patil Source: https://www.frontiersin.org/research-topics/78734/advances-in-quantifying-and-communicating-uncertainty-in-single-cell-biology