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Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. MultiDCoX is a space and time efficient procedure to identify differentially co-expressed gene sets and successfully identify influence of individual factors on differential co-expression.Īnalysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. MultiDCoX analysis of a breast cancer dataset identified interesting biologically meaningful differentially co-expressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression. Simulated data analysis demonstrates that the algorithm can effectively elicit differentially co-expressed (DCX) gene sets and quantify the influence of each factor on co-expression. We developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis. No algorithm or methodology is available for multi-factor analysis of differential co-expression. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies.
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It has been used to identify differential co-expression networks or interactomes. Differential co-expression (DCX) signifies change in degree of co-expression of a set of genes among different biological conditions.
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