Package Recipe 'bioconductor-treekor'

recipe bioconductor-treekor

Cytometry Cluster Hierarchy and Cellular-to-phenotype Associations

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

treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; quantify a cell types as a proportion relative to all cells in a sample (%total), and, as the proportion relative to a parent population (%parent); perform significance testing using the calculated proportions; and provide an interactive html visualisation to help highlight key results.

package bioconductor-treekor¶

Versions:

1.18.0-0, 1.14.0-0, 1.10.0-0, 1.8.0-0, 1.6.0-0, 1.2.0-0, 1.0.0-0

Depends:

on bioconductor-diffcyt >=1.30.0,<1.31.0
on bioconductor-edger >=4.8.0,<4.9.0
on bioconductor-ggtree >=4.0.0,<4.1.0
on bioconductor-hopach >=2.70.0,<2.71.0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0
on r-ape
on r-base >=4.5,<4.6.0a0
on r-data.table
on r-dplyr
on r-ggiraph
on r-ggplot2
on r-lme4
on r-multcomp
on r-patchwork
on r-tidyr

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-treekor

to add into an existing workspace instead, run:

pixi add bioconductor-treekor

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-treekor

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-treekor

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-treekor:<tag>

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