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cff-version: 1.2.0
message: 'To cite package "cellmig" in publications use:'
type: software
license: GPL-3.0-only
title: 'cellmig: Uncertainty-aware quantitative analysis of high-throughput live cell
  migration data'
version: 1.3.4
doi: 10.32614/CRAN.package.cellmig
abstract: High-throughput cell imaging 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. It further enables synthetic
  data generation for experimental design optimization.
authors:
- family-names: Kitanovski
  given-names: Simo
  email: simokitanovski@gmail.com
  orcid: https://orcid.org/0000-0003-2909-5376
repository: https://bioc.r-universe.dev
repository-code: https://github.com/snaketron/cellmig
commit: 8f5b204efeae7f7365e0db6384d746f108f4d441
url: https://github.com/snaketron/cellmig
date-released: '2026-05-23'
contact:
- family-names: Kitanovski
  given-names: Simo
  email: simokitanovski@gmail.com
  orcid: https://orcid.org/0000-0003-2909-5376