Package Recipe 'bioconductor-imcrtools'

recipe bioconductor-imcrtools

Methods for imaging mass cytometry data analysis

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/imcRtools.html
License:: GPL-3
Recipe:: /bioconductor-imcrtools/meta.yaml

This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected.

package bioconductor-imcrtools¶

Versions:

1.16.0-0, 1.12.0-0, 1.8.0-0, 1.6.3-0, 1.4.0-0, 1.0.0-0

Depends:

on bioconductor-biocneighbors >=2.4.0,<2.5.0
on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-cytomapper >=1.22.0,<1.23.0
on bioconductor-ebimage >=4.52.0,<4.53.0
on bioconductor-matrixgenerics >=1.22.0,<1.23.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-scuttle >=1.20.0,<1.21.0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0
on bioconductor-spatialexperiment >=1.20.0,<1.21.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on r-abind
on r-base >=4.5,<4.6.0a0
on r-concaveman
on r-data.table
on r-distances
on r-dplyr
on r-dt
on r-ggplot2
on r-ggraph
on r-igraph
on r-magrittr
on r-pheatmap
on r-readr
on r-rlang
on r-rtriangle
on r-sf
on r-stringr
on r-tidygraph
on r-tidyselect
on r-viridis
on r-vroom

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-imcrtools

to add into an existing workspace instead, run:

pixi add bioconductor-imcrtools

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-imcrtools

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-imcrtools

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-imcrtools:<tag>

(see bioconductor-imcrtools/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-imcrtools/README.html)