Background: Differential co-expression, complementary to differential expression, signifies change in degree of co-expression of a set of genes between different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms or methodologies 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. Results: We developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis using genome-wide gene expression 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 co-expressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression. Conclusions: Similar to differential expression, differential co-expression also needs to be analyzed in the context of multiple genetic and clinical factors. MultiDCoX is a space and time efficient procedure to identify differentially co-expressed gene sets and successfully identify the influence of individual factors on differential co-expression.