Browsing by Author "Chaba, Linda Akoth"

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    A copula-based approach to differential gene expression analysis
    Chaba, Linda Akoth; Odhiambo, John W.; Omolo, Bernard
    Melanoma is a major public health concern in the developed world. Melanoma research has been enhanced by the introduction of microarray technology, whose main aim is to identify genes that are associated with outcomes of interest in melanoma biology and disease progression. Many statistical methods have been proposed for gene selection but so far none of them is regarded as the standard method. In addition, none of the proposed methods have applied copulas to identify genes that are associated with quantitative traits. In this study, we developed a copula-based approach to identify genes that are associated with quantitative traits in the systems biology of melanoma. To assess the statistical properties of model , we evaluated the power, the false-rejection rate and the true-rejection rate using simulated gene expression data . The model was then applied to a melanoma dataset for validation. Comparison of the copula approach with the Bayesian and other parametric approaches was performed, based on the false discovery rate (FOR) , the value of R-square and prognostic properties. It turned out that the copula model was more robust and better than the others in the selection of genes that were biologically and clinically significant.
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    A Copula-based approach to differential gene expression analysis
    (Strathmore University, 2006) Chaba, Linda Akoth
    Microarray technology has revolutionized genomic studies by enabling the study of differential expression of thousands of genes simultaneously. The main objective in microarray experiments is to identify a panel of genes that are associated with a disease outcome or trait. In this thesis, we develop and evaluate a semi-parametric copula-based algorithm for gene selection that does not depend on the distributions of the covariates, except that their marginal distributions are continuous. A comparison of the developed method with the existing methods is done based on power to identify differentially expressed genes (DEGs) and control of Type I error rate via a simulation study. Simulations indicate that the copula-based model has a reasonable power in selecting differentially expressed gene and has a good control of Type I error rate. These results are validated in a publicly available melanoma dataset. The copula-based approach turns out to be useful in finding genes that are clinically important. Relaxing parametric assumptions on microarray data may yield procedures that have good power for differential gene expression analysis.