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treekoR

This is the development version of treekoR; for the stable release version, see treekoR.

Cytometry Cluster Hierarchy and Cellular-to-phenotype Associations


Bioconductor version: Development (3.19)

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.

Author: Adam Chan [aut, cre], Ellis Patrick [ctb]

Maintainer: Adam Chan <adam.s.chan at sydney.edu.au>

Citation (from within R, enter citation("treekoR")):

Installation

To install this package, start R (version "4.4") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("treekoR")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

Reference Manual PDF

Details

biocViews Clustering, DifferentialExpression, FlowCytometry, ImmunoOncology, MassSpectrometry, SingleCell, Software, StatisticalMethod, Visualization
Version 1.11.0
In Bioconductor since BioC 3.13 (R-4.1) (3 years)
License GPL-3
Depends R (>= 4.1)
Imports stats, utils, tidyr, dplyr, data.table, ggiraph, ggplot2, hopach, ape, ggtree, patchwork, SingleCellExperiment, diffcyt, edgeR, lme4, multcomp
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Suggests knitr, rmarkdown, BiocStyle, CATALYST, testthat (>= 3.0.0)
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package
Windows Binary
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macOS Binary (arm64)
Source Repository git clone https://git.bioconductor.org/packages/treekoR
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/treekoR
Package Short Url https://bioconductor.org/packages/treekoR/
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