Integrating Multiple Data Sources for Combinatorial Marker Discovery: A Study in Tumorigenesis

Identification of combinatorial markers from multiple data sources is a challenging task in bioinformatics. Here, we propose a novel computational framework for identifying significant combinatorial markers (<inline-formula> <tex-math notation="LaTeX">$SCM$</tex-math><alternatives><inline-graphic xlink:href="bandyopadhyay-ieq1-2636207.gif"/> </alternatives></inline-formula>s) using both gene expression and methylation data. The gene expression and methylation data are integrated into a single continuous data as well as a (post-discretized) boolean data based on their intrinsic (i.e., inverse) relationship. A novel combined score of methylation and expression data (viz., <inline-formula><tex-math notation="LaTeX">$CoMEx$</tex-math><alternatives> <inline-graphic xlink:href="bandyopadhyay-ieq2-2636207.gif"/></alternatives></inline-formula>) is introduced which is computed on the integrated continuous data for identifying initial non-redundant set of genes. Thereafter, (maximal) frequent closed homogeneous genesets are identified using a well-known biclustering algorithm applied on the integrated boolean data of the determined non-redundant set of genes. A novel sample-based weighted support ( <inline-formula><tex-math notation="LaTeX">$WS$</tex-math><alternatives> <inline-graphic xlink:href="bandyopadhyay-ieq3-2636207.gif"/></alternatives></inline-formula>) is then proposed that is consecutively calculated on the integrated boolean data of the determined non-redundant set of genes in order to identify the non-redundant significant genesets. The top few resulting genesets are identified as potential <inline-formula><tex-math notation="LaTeX">$SCM$</tex-math><alternatives> <inline-graphic xlink:href="bandyopadhyay-ieq4-2636207.gif"/></alternatives></inline-formula>s. Since our proposed method generates a smaller number of significant non-redundant genesets than those by other popular methods, the method is much faster than the others. Application of the proposed technique on an expression and a methylation data for Uterine tumor or Prostate Carcinoma produces a set of significant combination of markers. We expect that such a combination of markers will produce lower false positives than individual markers.

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