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Today, cell migration velocity can be measured in high-throughput assays that generate complex, hierarchically structured datasets with technical noise, batch effects, and biological variability, introducing significant uncertainty in velocity estimates. Current statistical approaches often fail to rigorously quantify this uncertainty, limiting reproducibility and comparisons across independent experimental datasets. Here, we present\n                    <jats:italic>cellmig<\/jats:italic>\n                    , a specialized computational tool that addresses this challenge. It implements established Bayesian hierarchical modeling within an accessible workflow tailored for high-throughput live cell migration assays, to separate biological signals from technical variation while explicitly quantifying uncertainty in migration velocity.\n                    <jats:italic>cellmig<\/jats:italic>\n                    provides a robust framework for analyzing cell migration assays, including dose-response studies and large-scale screens with multiple biological and technical replicates. By modeling biological variability (e.g., compound-dependent effects) and technical confounders (e.g., batch variability) within a unified Bayesian framework,\n                    <jats:italic>cellmig<\/jats:italic>\n                    estimates condition-specific effects on cell velocity with probabilistic uncertainty intervals, avoiding common pitfalls associated with null-hypothesis testing. Through exhaustive benchmarking against commonly used approaches in the field, we demonstrate that\n                    <jats:italic>cellmig<\/jats:italic>\n                    achieves improved sensitivity in detecting subtle migration effects and enhanced robustness against technical variability. Additionally, its generative models enable simulation of migration velocities under various assumptions, aiding experimental planning. We validated\n                    <jats:italic>cellmig<\/jats:italic>\n                    through a tiered strategy: (1) benchmarking on two independent experimental datasets and (2) deployment on a large-scale high-throughput screen that discovered new chemical biology. Our results demonstrate that\n                    <jats:italic>cellmig<\/jats:italic>\n                    can detect subtle dose-dependent velocity changes, maintain robustness against systematic variability and batch effects, and facilitate reliable integration of multi-experiment datasets. In summary,\n                    <jats:italic>cellmig<\/jats:italic>\n                    enhances reproducibility, reliability, and biological insight in high-throughput migration studies, facilitating quantitative inter-dataset comparisons.\n                    <jats:italic>cellmig<\/jats:italic>\n                    is implemented as an open-source R package and is freely available on Bioconductor (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/bioconductor.org\/packages\/cellmig\" xlink:type=\"simple\">https:\/\/bioconductor.org\/packages\/cellmig<\/jats:ext-link>\n                    ).\n                  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