CYBERTRACK2.0: zero-inflated model-based cell clustering and population tracking method for longitudinal mass cytometry data.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
12 07 2021
12 07 2021
Historique:
received:
24
03
2020
revised:
16
09
2020
accepted:
28
09
2020
pubmed:
15
10
2020
medline:
16
7
2021
entrez:
14
10
2020
Statut:
ppublish
Résumé
Recent advancements in high-dimensional single-cell technologies, such as mass cytometry, enable longitudinal experiments to track dynamics of cell populations and identify change points where the proportions vary significantly. However, current research is limited by the lack of tools specialized for analyzing longitudinal mass cytometry data. In order to infer cell population dynamics from such data, we developed a statistical framework named CYBERTRACK2.0. The framework's analytic performance was validated against synthetic and real data, showing that its results are consistent with previous research. CYBERTRACK2.0 is available at https://github.com/kodaim1115/CYBERTRACK2. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 33051653
pii: 5922807
doi: 10.1093/bioinformatics/btaa873
pmc: PMC8275981
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1632-1634Subventions
Organisme : JSPS Grant-in-Aid for Scientific Research
ID : 18H04798
Organisme : Japan Agency for Medical Research and Development
ID : JP19dm0107087h0004
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
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