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Computationally efficient multidimensional analysis of complex flow cytometry data using second order polynomial histograms
Cytometry. Part A, 2016-01, Vol.89 (1), p.44-58
Zaunders, John
Jing, Junmei
Leipold, Michael
Maecker, Holden
Kelleher, Anthony D.
Koch, Inge
2016
Details
Autor(en) / Beteiligte
Zaunders, John
Jing, Junmei
Leipold, Michael
Maecker, Holden
Kelleher, Anthony D.
Koch, Inge
Titel
Computationally efficient multidimensional analysis of complex flow cytometry data using second order polynomial histograms
Ist Teil von
Cytometry. Part A, 2016-01, Vol.89 (1), p.44-58
Ort / Verlag
United States: Wiley Subscription Services, Inc
Erscheinungsjahr
2016
Link zum Volltext
Quelle
Wiley Online Library Journals Frontfile Complete
Beschreibungen/Notizen
Many methods have been described for automated clustering analysis of complex flow cytometry data, but so far the goal to efficiently estimate multivariate densities and their modes for a moderate number of dimensions and potentially millions of data points has not been attained. We have devised a novel approach to describing modes using second order polynomial histogram estimators (SOPHE). The method divides the data into multivariate bins and determines the shape of the data in each bin based on second order polynomials, which is an efficient computation. These calculations yield local maxima and allow joining of adjacent bins to identify clusters. The use of second order polynomials also optimally uses wide bins, such that in most cases each parameter (dimension) need only be divided into 4–8 bins, again reducing computational load. We have validated this method using defined mixtures of up to 17 fluorescent beads in 16 dimensions, correctly identifying all populations in data files of 100,000 beads in <10 s, on a standard laptop. The method also correctly clustered granulocytes, lymphocytes, including standard T, B, and NK cell subsets, and monocytes in 9‐color stained peripheral blood, within seconds. SOPHE successfully clustered up to 36 subsets of memory CD4 T cells using differentiation and trafficking markers, in 14‐color flow analysis, and up to 65 subpopulations of PBMC in 33‐dimensional CyTOF data, showing its usefulness in discovery research. SOPHE has the potential to greatly increase efficiency of analysing complex mixtures of cells in higher dimensions. © 2015 International Society for Advancement of Cytometry
Sprache
Englisch
Identifikatoren
ISSN: 1552-4922
eISSN: 1552-4930
DOI: 10.1002/cyto.a.22704
Titel-ID: cdi_proquest_miscellaneous_1776647375
Format
–
Schlagworte
Adult
,
Algorithms
,
Automatic Data Processing - methods
,
B-Lymphocytes - cytology
,
Beads
,
Bins
,
Biomarkers - analysis
,
CD4 antigen
,
Cluster Analysis
,
Clustering
,
Color
,
complex data
,
Computational Biology - methods
,
Computational efficiency
,
Computer applications
,
Cytometry
,
data analysis
,
Data Interpretation, Statistical
,
Data points
,
Flow cytometry
,
Flow Cytometry - methods
,
Fluorescence
,
Granulocytes - cytology
,
high dimensions
,
Histograms
,
Humans
,
Immunological memory
,
Killer Cells, Natural - cytology
,
Leukocytes (granulocytic)
,
Lymphocytes
,
Lymphocytes B
,
Lymphocytes T
,
Maxima
,
Memory cells
,
Monocytes
,
Multivariate analysis
,
Natural killer cells
,
Peripheral blood mononuclear cells
,
Polynomials
,
Set theory
,
Shape memory
,
Subpopulations
,
T-Lymphocyte Subsets - cytology
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