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This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB06)

Open Access Research

Determination of the minimum number of microarray experiments for discovery of gene expression patterns

Fang-Xiang Wu12*, WJ Zhang12 and Anthony J Kusalik23

Author Affiliations

1 Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada

2 Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada

3 Department of Computer Science, University of Saskatchewan, Saskatoon, SK, S7N 5C9, Canada

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BMC Bioinformatics 2006, 7(Suppl 4):S13  doi:10.1186/1471-2105-7-S4-S13

Published: 12 December 2006

Abstract

Background

One type of DNA microarray experiment is discovery of gene expression patterns for a cell line undergoing a biological process over a series of time points. Two important issues with such an experiment are the number of time points, and the interval between them. In the absence of biological knowledge regarding appropriate values, it is natural to question whether the behaviour of progressively generated data may by itself determine a threshold beyond which further microarray experiments do not contribute to pattern discovery. Additionally, such a threshold implies a minimum number of microarray experiments, which is important given the cost of these experiments.

Results

We have developed a method for determining the minimum number of microarray experiments (i.e. time points) for temporal gene expression, assuming that the span between time points is given and the hierarchical clustering technique is used for gene expression pattern discovery. The key idea is a similarity measure for two clusterings which is expressed as a function of the data for progressive time points. While the experiments are underway, this function is evaluated. When the function reaches its maximum, it indicates the set of experiments reach a saturated state. Therefore, further experiments do not contribute to the discrimination of patterns.

Conclusion

The method has been verified with two previously published gene expression datasets. For both experiments, the number of time points determined with our method is less than in the published experiments. It is noted that the overall approach is applicable to other clustering techniques.