- 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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's version
- Files:
-
-
(pdf, 1.5mb)
-
- Publisher copy:
- 10.1214/18-AOAS1209
-
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- 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
- Copyright holder:
- Institute of Mathematical Statistics
- Copyright date:
- 2019
- Notes:
- Copyright © 2019 Institute of Mathematical Statistics.
Journal article
Sequential Dirichlet process mixtures of multivariate skew t-distributions for model-based clustering of flow cytometry data
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