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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 i...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's Version

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Publisher copy:
10.1038/s41598-019-45301-0

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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
Subgroup:
KIR
Role:
Author
ORCID:
0000-0003-1985-4914

Contributors

Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
Subgroup:
KIR
Role:
Contributor
ORCID:
0000-0003-1985-4914
Publisher:
Springer Nature Publisher's website
Journal:
Scientific Reports Journal website
Volume:
9
Pages:
Article: 8914
Publication date:
2019-06-20
Acceptance date:
2019-05-15
DOI:
ISSN:
2045-2322
Pubs id:
pubs:998893
URN:
uri:c6729832-27a6-465c-9064-f9bcb3e9b17b
UUID:
uuid:c6729832-27a6-465c-9064-f9bcb3e9b17b
Local pid:
pubs:998893
Keywords:

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