A data-driven clustering method for time course gene expression data

Nucleic Acids Research
Ping MaJun S Liu

Abstract

Gene expression over time is, biologically, a continuous process and can thus be represented by a continuous function, i.e. a curve. Individual genes often share similar expression patterns (functional forms). However, the shape of each function, the number of such functions, and the genes that share similar functional forms are typically unknown. Here we introduce an approach that allows direct discovery of related patterns of gene expression and their underlying functions (curves) from data without a priori specification of either cluster number or functional form. Smoothing spline clustering (SSC) models natural properties of gene expression over time, taking into account natural differences in gene expression within a cluster of similarly expressed genes, the effects of experimental measurement error, and missing data. Furthermore, SSC provides a visual summary of each cluster's gene expression function and goodness-of-fit by way of a 'mean curve' construct and its associated confidence bands. We apply this method to gene expression data over the life-cycle of Drosophila melanogaster and Caenorhabditis elegans to discover 17 and 16 unique patterns of gene expression in each species, respectively. New and previously describe...Continue Reading

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Citations

May 27, 2011·Environmental Science & Technology·Na Gou, April Z Gu
May 19, 2010·Proceedings of the National Academy of Sciences of the United States of America·Baiyu ZhouGrant O'Keefe
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Software Mentioned

FCM
MCLUST
SOM
GeneMerge
CAGED
SS Clust
SSC
RCEM

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