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Bioinformatics Advance Access published online on March 16, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp139
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©2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Paul D W Kirk 1,* and Michael P H Stumpf 1,*

1Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, SW7 2AZ.

*To whom correspondence should be addressed., E-mail: paul.kirk06{at}imperial.ac.uk, m.stumpf{at}imperial.ac.uk


  Abstract

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 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 Gaussian process regression bootstrapping to two data sets 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 ODE model of a cell signalling pathway. Although we find that the parameter estimates inferred from the original data set 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 Gaussian process regression bootstrapping is available from our website: http://www3.imperial.ac.uk/theoreticalsystemsbiology/data-software/

Contact: paul.kirk{at}imperial.ac.uk, m.stumpf{at}imperial.ac.uk

Associate Editor: Dr. Limsoon Wong

Received on November 21, 2008; revised on February 3, 2009; accepted on March 7, 2009

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