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bioRxiv · 10.1101/114397

MultiDCoX: Multi-factor Analysis of Differential Co-expression

Liany, H.·Rajapakse, J. C.·Karuturi, R. K. M.
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Abstract

BackgroundDifferential co-expression signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. No algorithm or methodology is available for multi-factor analysis of differential co-expression.\n\nResultsWe developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis of transcriptomic data. 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. MultiDCoX analysis of a breast cancer dataset identified interesting biologically meaningful differentially coexpressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression.\n\nConclusionsMultiDCoX 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.\n\nSoftwareR function will be available upon request.

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Liany, H., Rajapakse, J. C., Karuturi, R. K. M.. 2017-03-06. MultiDCoX: Multi-factor Analysis of Differential Co-expression. https://doi.org/10.1101/114397

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