Package Recipe 'bioconductor-cellmig'

recipe bioconductor-cellmig

Uncertainty-aware quantitative analysis of high-throughput live cell migration data

Homepage:: https://bioconductor.org/packages/3.22/bioc/html/cellmig.html
License:: GPL-3 + file LICENSE
Recipe:: /bioconductor-cellmig/meta.yaml

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.

package bioconductor-cellmig¶

Versions:

1.0.0-0

Depends:

on bioconductor-ggtree >=4.0.0,<4.1.0
on bioconductor-ggtree >=4.0.4,<4.1.0a0
on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-ape
on r-base >=4.5,<4.6.0a0
on r-bh >=1.66.0
on r-ggforce
on r-ggplot2
on r-patchwork
on r-rcpp >=0.12.0
on r-rcppeigen >=0.3.3.3.0
on r-rcppparallel >=5.0.1
on r-reshape2
on r-rstan >=2.18.1
on r-rstantools >=2.4.0
on r-scales
on r-stanheaders >=2.18.0
on tbb-devel >=2022.3.0,<2022.4.0a0

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-cellmig

to add into an existing workspace instead, run:

pixi add bioconductor-cellmig

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-cellmig

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-cellmig

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-cellmig:<tag>

(see bioconductor-cellmig/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-cellmig/README.html)