Journal article
pcaReduce: Hierarchical clustering of single cell transcriptional profiles
- Abstract:
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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
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(Version of record, pdf, 2.5MB)
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- Publisher copy:
- 10.1186/s12859-016-0984-y
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Authors
Funding
+ Li Ka Shing Foundation
More from this funder
Grant:
Oxford-Stanford Big Data for Human Health Seed Grant
+ Medical Research Council
More from this funder
Grant:
New Investigator Research Grant (MR/L001411/1
Bibliographic Details
- Publisher:
- BioMed Central Publisher's website
- Journal:
- BMC Bioinformatics Journal website
- Volume:
- 17
- Article number:
- 140
- Publication date:
- 2016-01-01
- Acceptance date:
- 2016-03-09
- DOI:
- ISSN:
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1471-2105
- Source identifiers:
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610656
Item Description
- Keywords:
- Pubs id:
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pubs:610656
- UUID:
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uuid:aa2748ea-2a94-48f0-82dc-4babeb532f66
- Local pid:
- pubs:610656
- Deposit date:
- 2016-03-17
Terms of use
- 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.
- Licence:
- CC Attribution (CC BY)
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