Abstract
Cluster analysis has been widely used to explore thousands of gene expressions from microarray analysis and identify a small number of similar genes (objects) for further detailed biological investigation. However, most clustering algorithms tend to identify loose clusters with too many genes. In this paper, we propose a Bayesian tight clustering method for time course gene expression data, which selects a small number of closely-related genes and constructs tight clusters only with these closely-related genes.
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Joo, Y., Casella, G. & Hobert, J. Bayesian model-based tight clustering for time course data. Comput Stat 25, 17–38 (2010). https://doi.org/10.1007/s00180-009-0159-7
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DOI: https://doi.org/10.1007/s00180-009-0159-7