Journal article
Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data
- Abstract:
- Pseudotime algorithms can be employed to extract latent temporal information from cross-sectional data sets allowing dynamic biological processes to be studied in situations where the collection of time series data is challenging or prohibitive. Computational techniques have arisen from single-cell 'omics and cancer modelling where pseudotime can be used to learn about cellular differentiation or tumour progression. However, methods to date typically implicitly assume homogeneous genetic, phenotypic or environmental backgrounds, which becomes limiting as data sets grow in size and complexity. We describe a novel statistical framework that learns how pseudotime trajectories can be modulated through covariates that encode such factors. We apply this model to both single-cell and bulk gene expression data sets and show that the approach can recover known and novel covariate-pseudotime interaction effects. This hybrid regression-latent variable model framework extends pseudotemporal modelling from its most prevalent area of single cell genomics to wider applications.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 3.8MB, Terms of use)
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- Publisher copy:
- 10.1038/s41467-018-04696-6
Authors
+ Medical Research Council
More from this funder
- Funding agency for:
- Yau, C
- Campbell, K
- Grant:
- 090532/Z/09/Z
- Doctoral studentship
- Publisher:
- Nature Publishing Group
- Journal:
- Nature Communications More from this journal
- Volume:
- 9
- Issue:
- 1
- Pages:
- 2442
- Publication date:
- 2017-06-22
- Acceptance date:
- 2018-05-17
- DOI:
- EISSN:
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2041-1723
- ISSN:
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2041-1723
- Pmid:
-
29934517
- Language:
-
English
- Keywords:
- Pubs id:
-
pubs:859940
- UUID:
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uuid:a3028cfd-376a-4a34-8eab-b0c977d9e657
- Local pid:
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pubs:859940
- Source identifiers:
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859940
- Deposit date:
-
2018-09-11
Terms of use
- Copyright holder:
- Campbell and Yau
- Copyright date:
- 2017
- Notes:
- © The Author(s) 2018. 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. The 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/.
- Licence:
- CC Attribution (CC BY)
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