Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype.
|Author||Paolo Martini <email@example.com>, Gabriele Sales <firstname.lastname@example.org>, Chiara Romualdi <email@example.com>|
|Date of publication||None|
|Maintainer||Paolo Martini <firstname.lastname@example.org>|
clipper: Dissect the pathway to find the path with the greatest...
clipperAllRoots: Dissect the pathway to find the path with the greatest...
cliqueMeanTest: Mean test for cliques.
cliqueMixedTest: Mean test for cliques.
cliquePairedTest: Paired mean test for cliques.
cliqueVarianceTest: Variance test for cliques.
deleteEdge: Remove an edge from 'graphNel' object.
easyClip: Easy clip analysis.
easyLook: Summarize clipper output.
getGraphEntryGenes: Extract all the possible entry point (genes with no entering...
getJunctionTreePaths: Extract the shortest paths along the junction tree of the...
nameCliques: Generate clique names from their own elements.
pathwayTest: Whole pathway test using qpipf.
plotInCytoscape: Plot a pathway graph in Cytoscape highlighting the relevant...
prunePaths: Summarize the paths obtained by clipper according to their...