Treekor

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

# 1. Construct the hierarchical tree of clusters using getClusterTree (hopach)
clust_tree <- getClusterTree(exprs, clusters, hierarchy_method="hopach")

# 2. Perform significance testing of cell subpopulations using testTree
tested_tree <- testTree(phylo=clust_tree$clust_tree, clusters=clusters, samples=samples, classes=classes, pos_class_name=NULL)

# 3. Extract significance results using getTreeResults
res_df <- getTreeResults(tested_tree)

# 4. Visualize results interactively using plotInteractiveHeatmap
plotInteractiveHeatmap(tested_tree, clust_med_df = clust_tree$median_freq, clusters=clusters)

# 5. Construct tree with average linkage, test, and plot
clust_tree_avg <- getClusterTree(exprs, clusters, hierarchy_method="average")
tested_tree_avg <- testTree(clust_tree_avg$clust_tree, clusters=clusters, samples=samples, classes=classes, pos_class_name=NULL)
plotInteractiveHeatmap(tested_tree_avg, clust_med_df = clust_tree_avg$median_freq, clusters=clusters)

# 6. Construct tree with hopach, test with edgeR, and plot
tested_tree_edgeR <- testTree(clust_tree$clust_tree, clusters=clusters, samples=samples, classes=classes, sig_test="edgeR", pos_class_name="COV")
plotInteractiveHeatmap(tested_tree_edgeR, clust_med_df = clust_tree$median_freq, clusters=clusters)

# 7. Extract cell proportions (%total and %parent) using getCellProp
prop_df <- getCellProp(phylo=clust_tree$clust_tree, clusters=clusters, samples=samples, classes=classes)

# 8. Extract marker geometric means per cell type using getCellGMeans
means_df <- getCellGMeans(clust_tree$clust_tree, exprs=exprs, clusters=clusters, samples=samples, classes=classes)

Inputs are single-cell expression matrix, cluster IDs, sample IDs, and clinical classes; outputs are tested tree objects, proportion data frames, and interactive heatmaps.

When to Use

• When analyzing single-cell cytometry or flow cytometry data with hierarchical cell populations.
• To find robust associations between cell subset proportions (both %total and %parent) and patient clinical outcomes.
• To extract marker geometric means per cell type across samples for downstream machine learning or visualization.

When NOT to Use

• For standard single-cell RNA-seq differential gene expression analysis within a single cell type; use DESeq2, edgeR, or limma instead.
• When cell populations do not exhibit a hierarchical or nested lineage structure.

Data Requirements

• exprs: Single-cell expression matrix ($n \times p$) where $p$ is the number of markers and $n$ is the number of cells.
• clusters: A vector of length $n$ representing the cell type or cluster of each cell (character or numeric).
• classes: A vector of length $n$ containing the patient outcome/class each cell belongs to.
• samples: A vector of length $n$ identifying the patient/sample each cell belongs to.

Key Parameters

• hierarchy_method ("hopach"): Method to construct the hierarchical tree. Options include "hopach" or any hclust method (e.g., "average", "complete", "ward.D").
• sig_test ("ttest"): Significance test to use. Options include "ttest", "GLMM", or "edgeR".
• pos_class_name (NULL): Name of the positive class of interest for testing (e.g., "COV").

Best Practices

• Use HOPACH (hierarchy_method="hopach") as the default method to automatically determine the tree structure of cell clusters.
• Measure both %parent (proportion relative to immediate parent node) and %total (proportion relative to all cells) to capture nested proportions.
• Use plotInteractiveHeatmap to simultaneously visualize the hierarchical tree (colored by test statistics) and the median marker expression heatmap.

Common Pitfalls

• Ignoring hierarchical structure: Only analyzing %total proportions, which overlooks nested subpopulation changes. Fix: Use testTree to calculate and test both %total and %parent proportions.
• Incorrect positive class specification: Specifying a non-existent class name in pos_class_name when using sig_test="edgeR". Fix: Ensure pos_class_name matches one of the unique values in the classes vector (e.g., "COV").

Alternatives

• diffcyt: For high-dimensional cytometry differential analysis using GLMM or edgeR without hierarchical tree structures.
• Citrus: For supervised identification of stratifying cell populations.
• scda: For general single-cell differential abundance analysis.

Citations

• De Biasi, S. et al. 2020. "Marked T cell activation, senescence, exhaustion and skewing in patients with COVID-19." Nature Communications.

References

• Homepage: bioconductor.org/packages/treekoR
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/treekoR/inst/doc/treekoR.html