treekoR

treekoR utilises the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points.

Key Features:

• Hierarchical tree derivation: Derives a hierarchical tree structure of cell clusters from single cell cytometry data.
• Proportion quantification: Quantifies cell types as a proportion relative to all cells in a sample (%total) and as a proportion relative to a parent population (%parent).
• Significance testing: Performs significance testing using the calculated proportions.

Scientific Applications:

• Cell subset-phenotype association: Identifies associations between cell subsets and patient clinical end points in single cell cytometry studies.
• Nested proportion recapitulation: Recapitulates nested proportions prevalent in manual gating workflows that are often overlooked in automatic clustering approaches.

Methodology:

treekoR derives a hierarchical tree structure of cell clusters, calculates cell type proportions as %total and %parent, and performs significance testing on these proportions.