Clustering Gene Expression Data Over Time
Author Information
Author(s): S. Déjean, P. Martin, A. Baccini, P. Besse
Primary Institution: Laboratoire de Statistique et Probabilités, UMR 5583, Université Paul Sabatier, Toulouse, France
Hypothesis
The study aims to provide a relevant clustering of gene expression temporal profiles by focusing on the shapes of the curves rather than on the absolute level of expression.
Conclusion
The study found that the clustering results were in agreement with existing literature on the effects of fasting on mouse liver, suggesting promising directions for future biological investigations.
Supporting Evidence
- The study monitored the expression of 200 genes at 11 time points during fasting.
- A heuristic approach was proposed to tune the spline smoothing parameter using statistical and biological considerations.
- Clusters were illustrated through principal component analysis and heatmap visualization.
Takeaway
Scientists looked at how genes change when mice fast and found patterns that match what we already know about fasting.
Methodology
The study combined spline smoothing and first derivative computation with hierarchical and partitioning clustering to analyze gene expression data.
Participant Demographics
Mice were used in the study.
Digital Object Identifier (DOI)
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