bioconductor-treekor

name

bioconductor-treekor

description

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; qua

treekoR

Workflows

Standard Workflow

Identify robust associations between hierarchical cell subsets and clinical outcomes from single-cell cytometry data.

library(treekoR)
library(SingleCellExperiment)

# Load data
data(COVIDSampleData)
sce <- DeBiasi_COVID_CD8_samp

exprs <- t(assay(sce, "exprs"))
clusters <- colData(sce)$cluster_id
classes <- colData(sce)$condition
samples <- colData(sce)sample_id


clust_tree  getClusterTreeexprs clusters hierarchy_method


tested_tree  testTreephyloclust_treeclust_tree clustersclusters samplessamples classesclasses pos_class_name


res_df  getTreeResultstested_tree


plotInteractiveHeatmaptested_tree clust_med_df  clust_treemedian_freq clustersclusters


clust_tree_avg  getClusterTreeexprs clusters hierarchy_method
tested_tree_avg  testTreeclust_tree_avgclust_tree clustersclusters samplessamples classesclasses pos_class_name
plotInteractiveHeatmaptested_tree_avg clust_med_df  clust_tree_avgmedian_freq clustersclusters


tested_tree_edgeR  testTreeclust_treeclust_tree clustersclusters samplessamples classesclasses sig_test pos_class_name
plotInteractiveHeatmaptested_tree_edgeR clust_med_df  clust_treemedian_freq clustersclusters


prop_df  getCellPropphyloclust_treeclust_tree clustersclusters samplessamples classesclasses


means_df  getCellGMeansclust_treeclust_tree exprsexprs clustersclusters samplessamples classesclasses