Journal article
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...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's version
Actions
Access Document
- Files:
-
-
(pdf, 1.5MB)
-
- Publisher copy:
- 10.1214/18-AOAS1209
Why is the content I wish to access not available via ORA?
Bibliographic data (the information relating to research outputs) and full-text items (e.g. articles, theses, reports, etc.) arrive in ORA from several different sources. Unfortunately we are not able to make available the full-text for every research output.
Please contact the ORA team (
) if you have queries regarding unavailable content OR if you are aware of a full-text copy we can make available.
) if you have queries regarding unavailable content OR if you are aware of a full-text copy we can make available.
Content may be unavailable for the following four reasons
-
Version unsuitable
-
We have not obtained a suitable full-text for a given research output. See this page for more information.
-
Recently completed
-
Sometimes content is held in ORA but is unavailable for a fixed period of time to comply with the policies and wishes of rights holders.
-
Permissions
-
All content made available in ORA should comply with relevant rights, such as copyright. See this page for more information.
-
Clearance
-
Some thesis volumes scanned as part of the digitisation scheme funded by Dr Leonard Polonsky are currently unavailable due to sensitive material or uncleared third-party copyright content. We are attempting to contact authors whose theses are affected.
Alternative access to the full-text
You may be able to access the full-text directly from the publisher's website using the 'Publisher Copy' link in the 'Links & Downloads' box from a research output's ORA record page. This method may require an institutional or individual subscription to the journal/resource.
Authors
Funding
+ Agence nationale de la recherche
More from this funder
Grant:
Bayesian Nonparametric Methods for Signal and Image Processing ANR-13-BS-03-0006-0
Bibliographic Details
- 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
Item Description
Terms of use
- Copyright holder:
- Institute of Mathematical Statistics
- Copyright date:
- 2019
- Notes:
- Copyright © 2019 Institute of Mathematical Statistics.
If you are the owner of this record, you can report an update to it here: Report update to this record