treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data
Autor: | Jean Yang, Emily Blyth, Adam S Chan, Wei Jiang, Ellis Patrick |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
QH301-705.5
CD8 Antigens Method High dimensional Computational biology CD8-Positive T-Lymphocytes Biology QH426-470 03 medical and health sciences 0302 clinical medicine Genetics Cluster Analysis Humans Biology (General) 030304 developmental biology 0303 health sciences Gene Expression Profiling COVID-19 High-Throughput Nucleotide Sequencing Flow Cytometry Phenotype Human genetics 030220 oncology & carcinogenesis Single-Cell Analysis Unsupervised clustering Cytometry |
Zdroj: | Genome Biology, Vol 22, Iss 1, Pp 1-14 (2021) Genome Biology |
Popis: | 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. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02526-5. |
Databáze: | OpenAIRE |
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