Journal article
Structure-preserving visualisation of high dimensional single-cell datasets
- Abstract:
- Single-cell technologies offer an unprecedented opportunity to effectively characterize cellular heterogeneity in health and disease. Nevertheless, visualisation and interpretation of these multi-dimensional datasets remains a challenge. We present a novel framework, ivis, for dimensionality reduction of single-cell expression data. ivis utilizes a siamese neural network architecture that is trained using a novel triplet loss function. Results on simulated and real datasets demonstrate that ivis preserves global data structures in a low-dimensional space, learns a parametric mapping function that naturally adds new data points to existing embeddings, and scales linearly to hundreds of thousands of cells. ivis is made publicly available through Python and R interfaces on https://github.com/beringresearch/ivis.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 3.4MB, Terms of use)
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- Publisher copy:
- 10.1038/s41598-019-45301-0
Authors
Contributors
+ Monaco, C
- Institution:
- University of Oxford
- Division:
- Medical Sciences Division
- Department:
- NDORMS
- Sub department:
- KIR
- Role:
- Contributor
- ORCID:
- 0000-0003-1985-4914
- Publisher:
- Springer Nature
- Journal:
- Scientific Reports More from this journal
- Volume:
- 9
- Article number:
- 8914
- Publication date:
- 2019-06-20
- Acceptance date:
- 2019-05-15
- DOI:
- ISSN:
-
2045-2322
- Keywords:
- Pubs id:
-
pubs:998893
- UUID:
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uuid:c6729832-27a6-465c-9064-f9bcb3e9b17b
- Local pid:
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pubs:998893
- Source identifiers:
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998893
- Deposit date:
-
2019-05-16
Terms of use
- Copyright holder:
- Drozdov et al
- Copyright date:
- 2019
- Notes:
- © The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
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