The Library
Inferring the perturbation time from biological time course data
Tools
Tools
Tools
Yang, Jing, Penfold, Christopher A., Grant, Murray R. and Rattray, Magnus (2016) Inferring the perturbation time from biological time course data. Bioinformatics, 32 (19). pp. 2956-2964. doi:10.1093/bioinformatics/btw329
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP_Bioinformatics-2016-Yang-2956-64.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons: Attribution-Noncommercial 4.0. Download (852Kb) |
Official URL: http://dx.doi.org/10.1093/bioinformatics/btw329
Abstract
Motivation: Time course data are often used to study the changes to a biological process after perturbation. Statistical methods have been developed to determine whether such a perturbation induces changes over time, e.g. comparing a perturbed and unperturbed time course dataset to uncover differences. However, existing methods do not provide a principled statistical approach to identify the specific time when the two time course datasets first begin to diverge after a perturbation; we call this the perturbation time. Estimation of the perturbation time for different variables in a biological process allows us to identify the sequence of events following a perturbation and therefore provides valuable insights into likely causal relationships.
Results: We propose a Bayesian method to infer the perturbation time given time course data from a wild-type and perturbed system. We use a non-parametric approach based on Gaussian Process regression. We derive a probabilistic model of noise-corrupted and replicated time course data coming from the same profile before the perturbation time and diverging after the perturbation time. The likelihood function can be worked out exactly for this model and the posterior distribution of the perturbation time is obtained by a simple histogram approach, without recourse to complex approximate inference algorithms. We validate the method on simulated data and apply it to study the transcriptional change occurring in Arabidopsis following inoculation with Pseudomonas syringae pv. tomato DC3000 versus the disarmed strain DC3000hrpA.
Availability and Implementation: An R package, DEtime, implementing the method is available at https://github.com/ManchesterBioinference/DEtime along with the data and code required to reproduce all the results.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QK Botany S Agriculture > SB Plant culture |
||||||||
| Divisions: | Faculty of Science > Centre for Systems Biology | ||||||||
| Library of Congress Subject Headings (LCSH): | Arabidopsis -- Vaccination , Pseudomonas syringae | ||||||||
| Journal or Publication Title: | Bioinformatics | ||||||||
| Publisher: | Oxford University Press | ||||||||
| ISSN: | 1367-4803 | ||||||||
| Official Date: | 1 October 2016 | ||||||||
| Dates: |
|
||||||||
| Date of first compliant deposit: | 10 January 2017 | ||||||||
| Volume: | 32 | ||||||||
| Number: | 19 | ||||||||
| Page Range: | pp. 2956-2964 | ||||||||
| DOI: | 10.1093/bioinformatics/btw329 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | Seventh Framework Programme (European Commission) (FP7), Medical Research Council (Great Britain) (MRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Engineering and Physical Sciences Research Council (EPSRC) | ||||||||
| Grant number: | Grant 305626 (FP7), MR/N00017X/1 (MRC), BB/F005806 (BBSRC), EP/I036575/1 (EPSRC) |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
