Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction

Markus Heinonen*, Olivier Guipaud, Fabien Milliat, Valerie Buard, Beatrice Micheau, Georges Tarlet, Marc Benderitter, Farida Zehraoui, Florence d'Alche-Buc

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Motivation: Identifying the set of genes differentially expressed along time is an important task in two-sample time course experiments. Furthermore, estimating at which time periods the differential expression is present can provide additional insight into temporal gene functions. The current differential detection methods are designed to detect difference along observation time intervals or on single measurement points, warranting dense measurements along time to characterize the full temporal differential expression patterns.

Results: We propose a novel Bayesian likelihood ratio test to estimate the differential expression time periods. Applying the ratio test to systems of genes provides the temporal response timings and durations of gene expression to a biological condition. We introduce a novel non-stationary Gaussian process as the underlying expression model, with major improvements on model fitness on perturbation and stress experiments. The method is robust to uneven or sparse measurements along time. We assess the performance of the method on realistically simulated dataset and compare against state-of-the-art methods. We additionally apply the method to the analysis of primary human endothelial cells under an ionizing radiation stress to study the transcriptional perturbations over 283 measured genes in an attempt to better understand the role of endothelium in both normal and cancer tissues during radiotherapy. As a result, using the cascade of differential expression periods, domain literature and gene enrichment analysis, we gain insights into the dynamic response of endothelial cells to irradiation.

Original languageEnglish
Pages (from-to)728-735
Number of pages8
JournalBioinformatics
Volume31
Issue number5
DOIs
Publication statusPublished - 1 Mar 2015
MoE publication typeA1 Journal article-refereed

Keywords

  • COURSE MICROARRAY EXPERIMENTS
  • PROCESS REGRESSION
  • RADIATION
  • SERIES
  • APOPTOSIS
  • PROFILES
  • COURSES

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