- Abstract:
-
Background: Advances in single cell genomics provide a way of routinely generating transcriptomics data at the single cell level. A frequent requirement of single cell expression analysis is the identification of novel patterns of heterogeneity across single cells that might explain complex cellular states or tissue composition. To date, classical statistical analysis tools have being routinely applied, but there is considerable scope for the development of novel statistical ... Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's version
- Files:
-
-
(pdf, 2.5MB)
-
- Publisher copy:
- 10.1186/s12859-016-0984-y
-
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.
- Grant:
- Oxford-Stanford Big Data for Human Health Seed Grant
- Grant:
- New Investigator Research Grant (MR/L001411/1)
- Publisher:
- BioMed Central Publisher's website
- Journal:
- BMC Bioinformatics Journal website
- Volume:
- 17
- Pages:
- Article: 140
- Publication date:
- 2016-01-01
- DOI:
- ISSN:
-
1471-2105
- URN:
-
uuid:aa2748ea-2a94-48f0-82dc-4babeb532f66
- Source identifiers:
-
610656
- Local pid:
- pubs:610656
- Copyright holder:
- Žurauskienė and Yau
- Copyright date:
- 2016
- Notes:
- Copyright © 2016 Žurauskienė and Yau. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Journal article
pcaReduce: Hierarchical clustering of single cell transcriptional profiles
Actions
Access Document
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
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
+ Li Ka Shing Foundation
More from this funder
+ Medical Research Council
More from this funder
Bibliographic Details
Item Description
Terms of use
If you are the owner of this record, you can report an update to it here: Report update to this record