The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - July/August (2011 vol.8)
pp: 1093-1107
Peter Tiňo , The University of Birmingham, Birmingham
Hongya Zhao , Anderson Cancer Center, The University of Texas, Houston
Hong Yan , City University of Hong Kong, Hong Kong
ABSTRACT
The effects of a drug on the genomic scale can be assessed in a three-color cDNA microarray with the three color intensities represented through the so-called hexaMplot. In our recent study, we have shown that the Hough Transform (HT) applied to the hexaMplot can be used to detect groups of coexpressed genes in the normal-disease-drug samples. However, the standard HT is not well suited for the purpose because 1) the assayed genes need first to be hard-partitioned into equally and differentially expressed genes, with HT ignoring possible information in the former group; 2) the hexaMplot coordinates are negatively correlated and there is no direct way of expressing this in the standard HT and 3) it is not clear how to quantify the association of coexpressed genes with the line along which they cluster. We address these deficiencies by formulating a dedicated probabilistic model-based HT. The approach is demonstrated by assessing effects of the drug Rg1 on homocysteine-treated human umbilical vein endothetial cells. Compared with our previous study, we robustly detect stronger natural groupings of coexpressed genes. Moreover, the gene groups show coherent biological functions with high significance, as detected by the Gene Ontology analysis.
INDEX TERMS
Three-color cDNA microarray, probabilistic Hough transform, gene coexpression, hexaMplot, gene ontology.
CITATION
Peter Tiňo, Hongya Zhao, Hong Yan, "Searching for Coexpressed Genes in Three-Color cDNA Microarray Data Using a Probabilistic Model-Based Hough Transform", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 4, pp. 1093-1107, July/August 2011, doi:10.1109/TCBB.2010.120
REFERENCES
[1] D. Amaratunga and J. Cabrera, Exploration and Analysis of DNA Microarray and Protein Array Data. Wiley-Interscience, 2004.
[2] C. Debouck and P. Goodfellow, “DNA Microarrays in Drug Discovery and Development,” Nature Genetics, vol. 21, pp. 48-50, 1999.
[3] D. Gresham, M. Dunham, and D. Botstein, “Comparing Whole Genomes Using DNA Microarrays,” Nature Rev. Genetics, vol. 9, pp. 291-302, 2008.
[4] Y. Cho, J. Meade, J. Walden, X. Chen, Z. Guo, and P. Liang, “Multicolor Fluorescent Differential Display,” Biotechniques, vol. 30, pp. 562-572, 2001.
[5] G. Tsangaris, A. Botsonis, and I.P.F. Tzortzatou-Stathopoulou, “Evaluation of Cadmium-Induced Transcriptome Alterations by Three Color cDNA Labeling Microarray Analysis on a T-Cell Line,” Toxicology, vol. 178, no. 2, pp. 135-160, 2002.
[6] H. Zhao, N. Wong, K.-T. Fang, and Y. Yue, “Use of Three-Color cDNA Microarray Experiments to Assess the Therapeutic and Side Effect of Drugs,” Chemometrics and Intelligent Laboratory Systems, vol. 82, nos. 1/2, pp. 31-36, 2006.
[7] H. Zhao and H. Yan, “HoughFeature, a Novel Method for Assessing Drug Effects in Three-Color cDNA Microarray Experiments,” BMC Bioinformatics, vol. 8, pp. 256-266, 2007.
[8] J. Illingworth and J. Kittler, “A Survey of the Hough Transform,” Computer Vision, Graphics, and Image Processing, vol. 44, pp. 87-116, 1988.
[9] E. Boyle, S. Weng, J. Gollub, H. Jin, D. Botstein, J. Cherry, and G. Sherlock, “GO::TermFinderopen Source Software for Accessing Gene Ontology Information and Finding Significantly Enriched Gene Ontology Terms Associated with a List of Genes,” Bioinformatics, vol. 20, no. 18, pp. 3710-3715, 2004.
[10] S. Draghici, P. Khatri, R. Martins, G. Ostermeier, and S. Krawetz, “Global Functional Profiling of Gene Expression,” Genomics, vol. 81, pp. 98-104, 2003.
[11] M. Ashburner et al., “Gene Ontology: Tool for the Unification of Biology,” Nature Genetics, vol. 25, pp. 25-29, 2000.
[12] R. Sealfon, M. Hibbs, C. Huttenhower, C. Myers, and O. Troyanskaya, “GOLEM: An Interactive Graph-Based Gene-Ontology Navigation and Analysis Tool,” BMC Bioinformatics, vol. 7, article no. 443, 2006.
[13] Q. Ji and R. Haralick, “An Optimal Bayesian Hough Transform for Line Detection,” Proc. Int'l Conf. Image Processing, pp. 691-695, 1999.
[14] Q. Ji and R. Haralick, “Error Propagation for the Hough Transform,” Pattern Recognition Letters, vol. 22, nos. 6/7, pp. 813-823, 2001.
[15] A. Bonci, T. Leo, and S. Longhi, “A Bayesian Approach to the Hough Transform for Line Detection,” IEEE Trans. Systems, Man and Cybernetics, Part A, vol. 35, no. 6, pp. 945-955, Nov. 2005.
[16] J. Illingworth, G. Jones, J. Kittler, M. Petrou, and J. Princen, “Robust Statistical Methods of 2D and 3D Image Description,” Annals of Math. and Artificial Intelligence, vol. 10, nos. 1/2, pp. 125-148, 1994.
[17] J. Princen, J. Illingowrth, and J. Kittler, “Hypothesis Testing: A Framework for Analyzing and Optimizing Hough Transform Performance,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 4, pp. 329-341, Apr. 1994.
[18] N. Toronto, B. Morse, D. Ventura, and K. Seppi, “The Hough Transform's Implicit Bayesian Foundation,” Proc. Int'l Conf. Image Processing, pp. 377-380, 2007.
[19] S. Dudoit, Y. Yang, M. Callow, and T. Speed, “Statistical Methods for Identifying Genes with Differential Expression in Replicated cDNA Microarray Experiments,” Statistica Sinica, vol. 12, no. 1, pp. 111-139, 2002.
[20] Y. Yang, S. Dudoit, P. Luu, D. Lin, V. Peng, J. Ngai, and T. Speed, “Normalization for cDNA Microarray Data: A Robust Composite Method Addressing Single and Multiple Slide Systematic Variation,” Nucleic Acids Research, vol. 30, no. 4, p. e15, 2002.
[21] G. Smyth, “Limma: Linear Models for Microarray Data,” Bioinformatics and Computational Biology Solutions Using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, and W. Huber, eds., pp. 397-120, Springer, 2005.
[22] D. Jiang, C. Tang, and A. Zhang, “Cluster Analysis for Gene Expression Data: A Survey,” IEEE Trans. Knowledge and Data Eng., vol. 16, no. 11, pp. 1370-1386, Nov. 2004.
[23] A. Bhattacharya and R.K. De, “Average Correlation Clustering Algorithm (ACCA) for Grouping of Co-Regulated Genes with Similar Pattern of Variation in their Expression Values,” J. Biomedical Informatics, vol. 43, pp. 560-568, 2010.
[24] P. Smyth, “Model Selection for Probabilistic Clustering Using Cross-Validatedlikelihood,” Statistics and Computing, vol. 10, no. 1, pp. 63-72, 2000.
[25] J. Buhmann, “Learning and Data Clustering,” Handbook of Brain Theory and Neural Networks, M. Arbib, ed. MIT Press, 1995.
14 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool