# cellmig: Uncertainty-aware quantitative analysis of high-throughput live cell migration data # Overview Cell imaging enables us to profile the migration speeds of cells under various treatment conditions, such as different chemical compounds at varying doses, and across distinct biological states, like cancerous versus healthy cells. However, these experiments are typically costly, which limits the number of biological replicates that can be included. Additionally, cell migration readouts are often noisy and susceptible to batch effects. As a result, the estimates of cell migration speeds tend to be highly uncertain, a challenge further exacerbated by the use of suboptimal statistical methods. cellmig addresses these issues by implementing hierarchical Bayesian models designed to handle noisy and sparse cell migration data. It accounts for potential batch effects and provides robust quantitative estimates of cell speeds in each sample, while also facilitating differential migration analysis across different treatments. Additionally, cellmig includes tools that assist in planning future experiments, answering critical questions such as: "How many replicates and cells are needed to reliably detect a given effect?" # How to use cellmig To install this package, start R and enter: ``` r library("devtools") devtools::install_github("snaketron/cellmig") ``` Case studies are provided in the directory /vignettes # Workflow & output