Identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data

High-throughput single cell technologies hold the promise of discovering novel cellular relationships with disease and necessitate the use of effective analytical workflows. When manual gating is used to define cell types, the gating hierarchy can be used to identify cell types whose abundances change relative to a parent population. This strategy allows subtle changes to be observed that could be missed if small subsets were compared to all measured cells. However, typical analyses that employ unsupervised clustering overlook the valuable hierarchical structure present in cell type definitions by exclusively quantifying the proportions of cell type clusters relative to all cells. We present treekoR, a framework that facilitates multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data -- as failing to do so can lead to missing important biological insights.