Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data

Kieran R Campbell and Christopher Yau ()
Additional contact information
Kieran R Campbell: University of Oxford
Christopher Yau: University of Oxford

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: 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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.nature.com/articles/s41467-018-04696-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04696-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-018-04696-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().