RMCL-ESA: A Novel Method to Detect Co-regulatory Functional Modules in Cancer
Considering the increasingly large scale of gene expression data, common module identification algorithms exist many problems, such as large search space and long running time. A novel co-regulatory modules identification algorithm RMCL-ESA (Regularized Markov Cluster & Explosion Search Algorithm) based on improved Markov cluster and explosion search strategy has been proposed. Improved Markov cluster is adapted to preprocess gene expression profiles through three subprocedure: expansion, inflation, prune, which filter redundant genes and save computational cost. Then, two-stage explosion search strategy has been explored for identifying co-regulatory modules. Comparing with existing methods on breast cancer and ovary cancer datasets from TCGA, CRMs (Co-regulatory Functional Modules) of RMCL-ESA include more significant biological function GO-terms and regulation pathways with high enrichment score.
KeywordsCo-regulatory modules Markov cluster Explosion search
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