CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2011 vol.8 Issue No.04 - July/August
Searching for Coexpressed Genes in Three-Color cDNA Microarray Data Using a Probabilistic Model-Based Hough Transform
Issue No.04 - July/August (2011 vol.8)
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
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.120
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.
Three-color cDNA microarray, probabilistic Hough transform, gene coexpression, hexaMplot, gene ontology.
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