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  "Title": "Uncertainty-aware quantitative analysis of high-throughput live\ncell migration data",
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  "Authors@R": "c(\nperson(\"Simo\", \"Kitanovski\", email = \"simokitanovski@gmail.com\",\nrole = c(\"aut\", \"cre\"), comment=c(ORCID=\"0000-0003-2909-5376\")))",
  "Description": "High-throughput cell imaging facilitates the analysis of\ncell migration across many wells treated under different\nbiological conditions. These workflows generate considerable\ntechnical noise and biological variability, and therefore\ntechnical and biological replicates are necessary, leading to\nlarge, hierarchically structured datasets, i.e., cells are\nnested within technical replicates that are nested within\nbiological replicates. Current statistical analyses of such\ndata usually ignore the hierarchical structure of the data and\nfail to explicitly quantify uncertainty arising from technical\nor biological variability. To address this gap, we present\ncellmig, an R package implementing Bayesian hierarchical models\nfor migration analysis. cellmig quantifies condition- specific\nvelocity changes (e.g., drug effects) while modeling nested\ndata structures and technical artifacts. It further enables\nsynthetic data generation for experimental design optimization.",
  "License": "GPL-3 + file LICENSE",
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  "biocViews": "SingleCell, CellBiology, Bayesian, ExperimentalDesign,\nSoftware, BatchEffect, Regression, Clustering",
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  "Date/Publication": "2026-05-23 08:56:57 UTC",
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  "Author": "Simo Kitanovski [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-2909-5376>)",
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