Automated Data Cleanup for Mass Cytometry.
Gaussian parameters
probability state Modeling
quality control
unattended analysis
Journal
Cytometry. Part A : the journal of the International Society for Analytical Cytology
ISSN: 1552-4930
Titre abrégé: Cytometry A
Pays: United States
ID NLM: 101235694
Informations de publication
Date de publication:
02 2020
02 2020
Historique:
received:
06
06
2019
revised:
24
09
2019
accepted:
07
10
2019
pubmed:
19
11
2019
medline:
19
8
2021
entrez:
19
11
2019
Statut:
ppublish
Résumé
Mass cytometry is an emerging technology capable of 40 or more correlated measurements on a single cell. The complexity and volume of data generated by this platform have accelerated the creation of novel methods for high-dimensional data analysis and visualization. A key step in any high-level data analysis is the removal of unwanted events, a process often referred to as data cleanup. Data cleanup as applied to mass cytometry typically focuses on elimination of dead cells, debris, normalization beads, true aggregates, and coincident ion clouds from raw data. We describe a probability state modeling (PSM) method that automatically identifies and removes these elements, resulting in FCS files that contain mostly live and intact events. This approach not only leverages QC measurements such as DNA, live/dead, and event length but also four additional pulse-processing parameters that are available on Fluidigm Helios™ and CyTOF® (Fluidigm, Markham, Canada) 2 instruments with software versions of 6.3 or higher. These extra Gaussian-derived parameters are valuable for detecting well-formed pulses and eliminating coincident positive ion clouds. The automated nature of this new routine avoids the subjectivity of other gating methods and results in unbiased elimination of unwanted events. © 2019 International Society for Advancement of Cytometry.
Identifiants
pubmed: 31737997
doi: 10.1002/cyto.a.23926
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
184-198Subventions
Organisme : NCI NIH HHS
ID : P30 CA016086
Pays : United States
Informations de copyright
© 2019 International Society for Advancement of Cytometry.
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