cellmig
This is the development version of cellmig; for the stable release version, see cellmig.
Uncertainty-aware quantitative analysis of high-throughput live cell migration data
Bioconductor version: Development (3.24)
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.
Author: Simo Kitanovski [aut, cre] 
Maintainer: Simo Kitanovski <simokitanovski at gmail.com>
citation("cellmig")):Simo Kitanovski. cellmig: Uncertainty-aware quantitative analysis of high-throughput live cell migration data. doi:10.18129/B9.bioc.cellmig, R package version 1.3.9, https://bioconductor.org/packages/cellmig.
Generated from the package metadata; it may differ from the package's own citation.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## The following initializes the development version of Bioconductor
BiocManager::install(version = "devel")
BiocManager::install("cellmig")For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("cellmig")| User Manual: cellmig | HTML | R Script |
| User Manual: cellmig | HTML | R Script |
| User Manual: Data Simulation | HTML | R Script |
| Reference Manual | ||
| NEWS | Text | |
| LICENSE | Text |
Details
| biocViews | BatchEffect, Bayesian, CellBiology, Clustering, ExperimentalDesign, Regression, SingleCell, Software |
| Version | 1.3.9 |
| In Bioconductor since | BioC 3.22 (R-4.5) (1 year) |
| License | GPL-3 + file LICENSE |
| Depends | R (>= 4.3.0) |
| Imports | base, ggplot2, ggforce, ggtree, patchwork, ape, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.18.1), rstantools (>= 2.4.0), stats, utils, scales |
| System Requirements | GNU make |
| URL | https://github.com/snaketron/cellmig |
| Bug Reports | https://github.com/snaketron/cellmig/issues |
See More
| Suggests | BiocStyle, knitr, testthat |
| Linking To | BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report, r-universe |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | cellmig_1.3.9.tar.gz |
| Windows Binary (x86_64) | cellmig_1.3.9.zip (64-bit only) |
| macOS Binary (big-sur-x86_64) | cellmig_1.3.9.tgz |
| macOS Binary (sonoma-arm64) | cellmig_1.3.9.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/cellmig |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/cellmig |
| Package Short Url | https://bioconductor.org/packages/cellmig/ |
| Package Downloads Report | Download Stats |