CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.04 - July-Aug.
Issue No.04 - July-Aug. (2013 vol.10)
Tianwei Yu , Dept. of Biostat. & Bioinf., Emory Univ., Atlanta, GA, USA
Hesen Peng , Dept. of Biostat. & Bioinf., Emory Univ., Atlanta, GA, USA
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.99
High-throughput expression technologies, including gene expression array and liquid chromatography--mass spectrometry (LC-MS) and so on, measure thousands of features, i.e., genes or metabolites, on a continuous scale. In such data, both linear and nonlinear relations exist between features. Nonlinear relations can reflect critical regulation patterns in the biological system. However, they are not identified and utilized by traditional clustering methods based on linear associations. Clustering based on general dependences, i.e., both linear and nonlinear relations, is hampered by the high dimensionality and high noise level of the data. We developed a sensitive nonparametric measure of general dependence between (groups of) random variables in high dimensions. Based on this dependence measure, we developed a hierarchical clustering method. In simulation studies, the method outperformed correlation- and mutual information (MI)-based hierarchical clustering methods in clustering features with nonlinear dependences. We applied the method to a microarray data set measuring the gene expression in cell-cycle time series to show it generates biologically relevant results. The R code is available at http://userwww.service.emory.edu/~tyu8/GDHC.
statistical analysis, bioinformatics, cellular biophysics, genetics, lab-on-a-chip, cell-cycle time series, high- throughput expression data, general dependence sensitive nonparametric measure, high-throughput expression technology, gene expression array, liquid chromatography-mass spectrometry, LC-MS method, metabolite, feature linear relation, feature nonlinear relation, biological system critical regulation pattern, linear association, data high dimensionality effect, data high noise level effect, high dimension random variable, simulation study, correlation-based hierarchical clustering method, mutual information-based hierarchical clustering method, feature nonlinear dependence clustering, microarray data set, gene expression measurement, Noise, Vectors, Couplings, Clustering methods, Random variables, Standards, Bioinformatics, similarity measures, Algorithms, clustering
Tianwei Yu, Hesen Peng, "Hierarchical Clustering of High- Throughput Expression Data Based on General Dependences", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 4, pp. 1080-1085, July-Aug. 2013, doi:10.1109/TCBB.2013.99