treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data

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 v...

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Autores principales:	Adam Chan, Wei Jiang, Emily Blyth, Jean Yang, Ellis Patrick
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Biology (General) QH301-705.5 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/7b444117c64c4832824245384f195012
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spelling	oai:doaj.org-article:7b444117c64c4832824245384f1950122021-12-05T12:25:36ZtreekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data10.1186/s13059-021-02526-51474-760Xhttps://doaj.org/article/7b444117c64c4832824245384f1950122021-11-01T00:00:00Zhttps://doi.org/10.1186/s13059-021-02526-5https://doaj.org/toc/1474-760XAbstract 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.Adam ChanWei JiangEmily BlythJean YangEllis PatrickBMCarticleBiology (General)QH301-705.5GeneticsQH426-470ENGenome Biology, Vol 22, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5 Genetics QH426-470
spellingShingle	Biology (General) QH301-705.5 Genetics QH426-470 Adam Chan Wei Jiang Emily Blyth Jean Yang Ellis Patrick treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data
description	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.
format	article
author	Adam Chan Wei Jiang Emily Blyth Jean Yang Ellis Patrick
author_facet	Adam Chan Wei Jiang Emily Blyth Jean Yang Ellis Patrick
author_sort	Adam Chan
title	treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data
title_short	treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data
title_full	treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data
title_fullStr	treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data
title_full_unstemmed	treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data
title_sort	treekor: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data
publisher	BMC
publishDate	2021
url	https://doaj.org/article/7b444117c64c4832824245384f195012
work_keys_str_mv	AT adamchan treekoridentifyingcellulartophenotypeassociationsbyelucidatinghierarchicalrelationshipsinhighdimensionalcytometrydata AT weijiang treekoridentifyingcellulartophenotypeassociationsbyelucidatinghierarchicalrelationshipsinhighdimensionalcytometrydata AT emilyblyth treekoridentifyingcellulartophenotypeassociationsbyelucidatinghierarchicalrelationshipsinhighdimensionalcytometrydata AT jeanyang treekoridentifyingcellulartophenotypeassociationsbyelucidatinghierarchicalrelationshipsinhighdimensionalcytometrydata AT ellispatrick treekoridentifyingcellulartophenotypeassociationsbyelucidatinghierarchicalrelationshipsinhighdimensionalcytometrydata
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treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data

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