Clustering of gene expression data based on shape similarity

Authors:
Travis J. Hestilow;Yufei Huang
Affiliations:
Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX;Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX and Greehey Children's Cancer Research Institute, University of Texas Health Science Cent ...
Venue:
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Year:
2009

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Abstract

A method for gene clustering from expression profiles using shape information is presented. The conventional clustering approaches such as K-means assume that genes with similar functions have similar expression levels and hence allocate genes with similar expression levels into the same cluster. However, genes with similar function often exhibit similarity in signal shape even though the expression magnitude can be far apart. Therefore, this investigation studies clustering according to signal shape similarity. This shape information is captured in the form of normalized and time-scaled forward first differences, which then are subject to a variational Bayes clustering plus a non-Bayesian (Silhouette) cluster statistic. The statistic shows an improved ability to identify the correct number of clusters and assign the components of cluster. Based on initial results for both generated test data and Escherichia coli microarray expression data and initial validation of the Escherichia coli results, it is shown that the method has promise in being able to better cluster time-series microarray data according to shape similarity.