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Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives

Abstract

Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 72 hours of fasting. The aim of this study was to provide a relevant clustering of gene expression temporal profiles. This was achieved by focusing on the shapes of the curves rather than on the absolute level of expression. Actually, we combined spline smoothing and first derivative computation with hierarchical and partitioning clustering. A heuristic approach was proposed to tune the spline smoothing parameter using both statistical and biological considerations. Clusters are illustrated a posteriori through principal component analysis and heatmap visualization. Most results were found to be in agreement with the literature on the effects of fasting on the mouse liver and provide promising directions for future biological investigations.

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Correspondence to S Déjean.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Déjean, S., Martin, P., Baccini, A. et al. Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives. J Bioinform Sys Biology 2007, 70561 (2007). https://doi.org/10.1155/2007/70561

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  • DOI: https://doi.org/10.1155/2007/70561

Keywords

  • Gene Expression Data
  • Mouse Liver
  • Heuristic Approach
  • Absolute Level
  • Smoothing Parameter