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Treffer: Inferring the perturbation time from biological time course data.

Title:
Inferring the perturbation time from biological time course data.
Authors:
Yang J; Faculty of Life Sciences, University of Manchester, Manchester, UK., Penfold CA; Warwick Systems Biology Centre, University of Warwick, Coventry, UK., Grant MR; School of Biosciences, University of Exeter, Exeter, UK., Rattray M; Faculty of Life Sciences, University of Manchester, Manchester, UK.
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2016 Oct 01; Vol. 32 (19), pp. 2956-64. Date of Electronic Publication: 2016 Jun 10.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Models, Statistical*, Algorithms ; Arabidopsis/genetics ; Bayes Theorem ; Models, Genetic ; Pseudomonas syringae/genetics ; Transcription, Genetic
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Grant Information:
MR/N00017X/1 United Kingdom MRC_ Medical Research Council; BB/F005806/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council
Entry Date(s):
Date Created: 20160612 Date Completed: 20170815 Latest Revision: 20250529
Update Code:
20250530
PubMed Central ID:
PMC5039917
DOI:
10.1093/bioinformatics/btw329
PMID:
27288495
Database:
MEDLINE

Weitere Informationen

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.
Contact: Jing.Yang@manchester.ac.uk or Magnus.Rattray@manchester.ac.uk
Supplementary Information: Supplementary data are available at Bioinformatics online.
(© The Author 2016. Published by Oxford University Press.)