# cellmig: Uncertainty-aware quantitative analysis of high-throughput live cell migration data # Background High-throughput tracking of cells with time-lapse microscopy followed by the acquisition of images at fixed time intervals facilitates the analysis of cell migration across many wells treated under different biological conditions. These workflows generate considerable technical noise and biological variability, and therefore technical and biological replicates are necessary, leading to large, hierarchically structured datasets, i.e., cells are nested within technical replicates that are nested within biological replicates. Current statistical analyses of such data usually ignore the hierarchical structure of the data and fail to explicitly quantify uncertainty arising from technical or biological variability. To address this gap, we present **cellmig**, an R package implementing Bayesian hierarchical models for migration analysis. **cellmig** quantifies condition-specific velocity changes (e.g., drug effects) while modeling nested data structures and technical artifacts, providing uncertainty-aware estimates through credible intervals. Furthermore, **cellmig** includes functionality for simulating synthetic datasets. These simulations are invaluable for experimental planning, allowing researchers to assess how different experimental designs (e.g., varying numbers of biological replicates, technical replicates, or cells per well) affect the precision of treatment effect estimates. # Installation To install this package, start R (version "4.5") and enter: ```{r, eval=FALSE} if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("cellmig") ``` Case studies are provided in the directory /vignettes # How to cite bioRxiv preprint doi: https://doi.org/10.1101/2025.06.12.659342