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Sequential Dirichlet process mixtures of multivariate skew t-distributions for model-based clustering of flow cytometry data

Abstract:

Flow cytometry is a high-throughput technology used to quantify multiple surface and intracellular markers at the level of a single cell. This enables us to identify cell subtypes and to determine their relative proportions. Improvements of this technology allow us to describe millions of individual cells from a blood sample using multiple markers. This results in high-dimensional datasets, whose manual analysis is highly time-consuming and poorly reproducible. While several methods have been...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1214/18-AOAS1209

Authors


Hejblum, B More by this author
Alkhassim, C More by this author
Gottardo, R More by this author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Oxford college:
Keble College
ORCID:
0000-0002-3952-224X
Thiebaut, R More by this author
More from this funder
Grant:
Bayesian Nonparametric Methods for Signal and Image Processing ANR-13-BS-03-0006-0
Publisher:
Institute of Mathematical Statistics Publisher's website
Journal:
Annals of Applied Statistics Journal website
Volume:
13
Issue:
1
Pages:
638-660
Publication date:
2019-04-10
Acceptance date:
2018-09-05
DOI:
EISSN:
1941-7330
ISSN:
1932-6157
Pubs id:
pubs:930504
URN:
uri:c71b8430-b62f-4270-b31d-53108b2dde18
UUID:
uuid:c71b8430-b62f-4270-b31d-53108b2dde18
Local pid:
pubs:930504

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