Uncertainty-aware quantitative analysis of high-throughput live cell migration data

Abstract

Accurate quantification of cell migration velocity is essential for understanding biological processes such as development, immune function, and cancer metastasis. High-throughput migration assays generate complex, hierarchically structured datasets with technical noise, batch effects, and biological variability, introducing uncertainty into velocity estimates that current methods often fail to quantify. To address this, we present cellmig, a computational tool using Bayesian hierarchical modeling to separate biological signals from technical variation while explicitly quantifying uncertainty in migration velocity. cellmig provides a robust framework for analyzing migration assays, including dose-response studies and large-scale screens with biological and technical replicates. By modeling biological variability and technical confounders within a unified Bayesian framework, it estimates condition-specific effects (e.g., drug effects) on cell velocity with probabilistic uncertainty intervals, avoiding common pitfalls of null-hypothesis testing. Its generative models simulate migration under various assumptions, aiding experimental planning. Overall, cellmig improves reproducibility and comparability across studies, offering deeper biological insight.