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Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers

Abstract:

Modeling bifurcations in single-cell transcriptomics data has become an increasingly popular field of research. Several methods have been proposed to infer bifurcation structure from such data, but all rely on heuristic non-probabilistic inference. Here we propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analyzers. Our model exhibits competitive performance on large datasets despite implementing full Markov-Chain Mon...

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

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Publisher copy:
10.12688/wellcomeopenres.11087.1

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Department:
Oxford, MSD, Physiology Anatomy & Genetics
Role:
Author
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Department:
Oxford, MSD, NDM, Human Genetics Wt Centre
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Author
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Funding agency for:
Campbell, KR
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Funding agency for:
Yau, C
Publisher:
F1000Research Publisher's website
Journal:
Wellcome Open Research Journal website
Volume:
2
Issue:
19
Publication date:
2017-03-15
Acceptance date:
2017-03-15
DOI:
ISSN:
2398-502X
Pubs id:
pubs:695835
URN:
uri:f1d36621-762e-4e17-8e5c-f69430e86888
UUID:
uuid:f1d36621-762e-4e17-8e5c-f69430e86888
Local pid:
pubs:695835

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