recipe bioconductor-treekor

Cytometry Cluster Hierarchy and Cellular-to-phenotype Associations

Homepage:

https://bioconductor.org/packages/3.16/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:

1.6.0-0,  1.2.0-0,  1.0.0-0

Depends:

• bioconductor-diffcyt >=1.18.0,<1.19.0

• bioconductor-edger >=3.40.0,<3.41.0

• bioconductor-ggtree >=3.6.0,<3.7.0

• bioconductor-hopach >=2.58.0,<2.59.0

• bioconductor-singlecellexperiment >=1.20.0,<1.21.0

• r-ape

• r-base >=4.2,<4.3.0a0

• r-data.table

• r-dplyr

• r-ggiraph

• r-ggplot2

• r-lme4

• r-multcomp

• r-patchwork

• r-tidyr

Required By:

Installation

With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-treekor

and update with:

conda update bioconductor-treekor

or use the docker container:

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

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

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)