Pattern Differentiations and Formulations for Heterogeneous Genomic Data through Hybrid Approaches

Abstract

Pattern differentiations and formulations are two main research tracks for heterogeneous genomic data pattern analysis. In this chapter, we develop hybrid methods to tackle the major challenges of power and reproducibility of the dynamic differential gene temporal patterns. The significant differentially expressed genes are selected not only from significant statistical analysis of microarrays but also supergenes resulting from singular value decomposition for extracting the gene components which can maximize the total predictor variability. Furthermore, hybrid clustering methods are developed based on resulting profiles from several clustering methods. We demonstrate the developed hybrid analysis through an application to a time course gene expression data from interferon-b-1a treated multiple sclerosis patients. The resulting integrated-condensed clusters and overrepresented gene lists demonstrate that the hybrid methods can successfully be applied. The post analysis includes function analysis and pathway discovery to validate the findings of the hybrid methods.