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Inferring the perturbation time from biological time course data.

Jing Yang1, Christopher A Penfold2, Murray R Grant3

  • 1Faculty of Life Sciences, University of Manchester, Manchester, UK.

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Summary

We developed a Bayesian method to pinpoint the exact time biological systems diverge after a perturbation. This helps understand causal relationships and the sequence of biological events.

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Area of Science:

  • * Systems Biology
  • * Genomics
  • * Bioinformatics

Background:

  • * Analyzing time course data is crucial for understanding biological responses to perturbations.
  • * Current statistical methods lack a principled approach to identify the precise onset of divergence between perturbed and unperturbed systems.
  • * Pinpointing the 'perturbation time' is key to inferring causal relationships and event sequences.

Purpose of the Study:

  • * To propose a novel Bayesian statistical method for inferring the perturbation time from time course data.
  • * To enable the identification of when biological systems begin to diverge following an external influence.
  • * To provide insights into the temporal dynamics and causal links in biological processes.

Main Methods:

  • * A non-parametric Bayesian approach utilizing Gaussian Process regression.
  • * Development of a probabilistic model for noise-corrupted, replicated time course data exhibiting divergence post-perturbation.
  • * Exact likelihood calculation and posterior distribution inference for perturbation time via a histogram approach.

Main Results:

  • * Successfully inferred perturbation times using the proposed Bayesian method on simulated data.
  • * Applied the method to analyze transcriptional changes in Arabidopsis post-inoculation with bacterial strains.
  • * The DEtime R package provides an accessible implementation of the developed method.

Conclusions:

  • * The proposed Bayesian method accurately estimates perturbation times, offering a significant advancement over existing techniques.
  • * Identifying the perturbation time facilitates the elucidation of causal relationships and the sequence of biological events.
  • * The DEtime package is available for researchers to analyze their own time course data.