DEGraph: Two-sample tests on a graph

DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results.

Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("DEGraph")
AuthorLaurent Jacob, Pierre Neuvial and Sandrine Dudoit
Bioconductor views DecisionTree DifferentialExpression GraphAndNetwork Microarray Network NetworkEnrichment
Date of publicationNone
MaintainerLaurent Jacob <laurent.jacob@gmail.com>
LicenseGPL-3
Version1.28.0

View on Bioconductor

Functions

annLoi2008 Man page
AN.test Man page
BS.test Man page
classLoi2008 Man page
exprLoi2008 Man page
getConnectedComponentList Man page
getKEGGPathways Man page
getSignedGraph Man page
graph.T2.test Man page
grListKEGG Man page
hyper.test Man page
laplacianFromA Man page
plotValuedGraph Man page
randomWAMGraph Man page
testOneConnectedComponent Man page
testOneGraph Man page
twoSampleFromGraph Man page
writeAdjacencyMatrix2KGML Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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