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A general algorithm for identifying high weight subnetworks in a vertex-weighted network. HotNet was developed for identifying significantly mutated groups of interacting genes from large cancer sequencing studies. HotNet uses an “insulated” heat diffusion process to simultaneously analyze a gene’s mutations (or mutation score) and its local topology. This diffusion process encodes the source, or directionality, of heat within the network, allowing HotNet to uncover surprisingly “hot” subnetworks with wide ranges of heat scores.
ESEA / Edge Set Enrichment Analysis
Identifies dysregulated pathways. ESEA is an edge-centric method that investigates the changes of inherent biological relationships embedded in pathways in the context of gene expression data. This method consists of: (i) converting pathways into graphs and constructing the background set of edges based on the converted graphs; (ii) estimating differential correlation scores of edges in the context of gene expression data; and (iii) calculating the edge enrichment score for each pathway in the pathway database.
DEGAS / DysrEgulated Gene set Analysis via Subnetworks
A method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. DEGAS was applied to seven human diseases and obtained statistically significant results that appear to home in on compact pathways enriched with hallmarks of the diseases. In Parkinson's disease, we provide novel evidence for involvement of mRNA splicing, cell proliferation, and the 14-3-3 complex in the disease progression. DEGAS is available as part of the MATISSE software package.
GiGA / Graph-based iterative Group Analysis
A fast and flexible delimitation of the most interesting areas in a microarray experiment. GiGA identifies all relevant physiological processes, puts them into context, summarizes them in an intuitive format, and associates them with the underlying evidence. It can be applied to experiments with very small numbers of replicates (a single time point in the diauxic shift test case) and can be used with any available functional annotation, including protein interaction networks, co-expression data or literature mining results, as well as in areas beyond microarray analysis. GiGA can be used as a stand-alone tool, but we expect that it will be most useful when integrated into existing microarray analysis software, and for that reason the GiGA algorithm is freely available without restrictions.
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