treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data.
Genome Biol
; 22(1): 324, 2021 11 29.
Article
in English
| MEDLINE | ID: covidwho-1745431
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Phenotype
/
Flow Cytometry
Limits:
Humans
Language:
English
Journal:
Genome Biol
Journal subject:
Molecular Biology
/
Genetics
Year:
2021
Document Type:
Article
Affiliation country:
S13059-021-02526-5
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