ArticleGenome biology2021
treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data.Nature communications · 2026Article
- Kontextual reframes analysis of spatial omics data and reveals consistent cell relationships across images.Cell reports methods · 2025Article
- scFocus: Detecting branching probabilities in single-cell data with SAC.Computational and structural biotechnology journal · 2025Article
- Construction and optimization of multi-platform precision pathways for precision medicine.Scientific reports · 2024Article
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Authors and funding
5 authors.
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Abstract
High-throughput single-cell technologies hold the promise of discovering novel cellular relationships with disease. However, analytical workflows constructed for these technologies to associate cell proportions with disease often employ unsupervised clustering techniques that overlook the valuable hierarchical structures that have been used to define cell types. We present treekoR, a framework that empirically recapitulates these structures, facilitating 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.
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