Journal Article

Gaussian process regression bootstrapping: exploring the effects of uncertainty in time course data

Paul D. W. Kirk and Michael P. H. Stumpf

in Bioinformatics

Volume 25, issue 10, pages 1300-1306
Published in print May 2009 | ISSN: 1367-4803
Published online March 2009 | e-ISSN: 1460-2059 | DOI:

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Motivation: Although widely accepted that high-throughput biological data are typically highly noisy, the effects that this uncertainty has upon the conclusions we draw from these data are often overlooked. However, in order to assign any degree of confidence to our conclusions, we must quantify these effects. Bootstrap resampling is one method by which this may be achieved. Here, we present a parametric bootstrapping approach for time-course data, in which Gaussian process regression (GPR) is used to fit a probabilistic model from which replicates may then be drawn. This approach implicitly allows the time dependence of the data to be taken into account, and is applicable to a wide range of problems.

Results: We apply GPR bootstrapping to two datasets from the literature. In the first example, we show how the approach may be used to investigate the effects of data uncertainty upon the estimation of parameters in an ordinary differential equations (ODE) model of a cell signalling pathway. Although we find that the parameter estimates inferred from the original dataset are relatively robust to data uncertainty, we also identify a distinct second set of estimates. In the second example, we use our method to show that the topology of networks constructed from time-course gene expression data appears to be sensitive to data uncertainty, although there may be individual edges in the network that are robust in light of present data.

Availability: Matlab code for performing GPR bootstrapping is available from our web site:


Supplementary information:Supplementary data are available at Bioinformatics online.

Journal Article.  5494 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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