Package: DEGraph 1.59.0

Laurent Jacob

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.

Authors:Laurent Jacob, Pierre Neuvial and Sandrine Dudoit

DEGraph_1.59.0.tar.gz
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DEGraph.pdf |DEGraph.html
DEGraph/json (API)
NEWS

# Install 'DEGraph' in R:
install.packages('DEGraph', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • annLoi2008 - Annotation data used in the DEGraph package vignette
  • classLoi2008 - Tamoxifen treatment resistance status data used in the DEGraph package vignette
  • exprLoi2008 - Gene expression data used in the DEGraph package vignette
  • grListKEGG - KEGG networks used in the DEGraph package vignette

On BioConductor:DEGraph-1.59.0(bioc 3.21)DEGraph-1.58.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

microarraydifferentialexpressiongraphandnetworknetworknetworkenrichmentdecisiontree

4.34 score 11 scripts 210 downloads 15 mentions 14 exports 58 dependencies

Last updated 2 months agofrom:6c12c512ea. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 29 2024
R-4.5-winNOTENov 29 2024
R-4.5-linuxNOTENov 29 2024
R-4.4-winNOTENov 29 2024
R-4.4-macNOTENov 29 2024
R-4.3-winNOTENov 29 2024
R-4.3-macNOTENov 29 2024

Exports:AN.testBS.testgetConnectedComponentListgetKEGGPathwaysgetSignedGraphgraph.T2.testhyper.testlaplacianFromAplotValuedGraphrandomWAMGraphtestOneConnectedComponenttestOneGraphtwoSampleFromGraphwriteAdjacencyMatrix2KGML

Dependencies:askpassbackportsbase64encbase64urlBHBiocGenericsbitopscaToolsclicrayoncurlDEoptimRdigestevaluatefansifastmapfsgenericsgluegplotsgraphgtoolshtmltoolshttrIRdisplayIRkerneljsonliteKEGGgraphKernSmoothlatticelifecyclemimemvtnormNCIgraphopensslpbdZMQpcaPPpillarR.methodsS3R.ooR.utilsR6RBGLRColorBrewerRCurlRCy3reprRgraphvizRJSONIOrlangrobustbaserrcovstringisysutf8uuidvctrsXML

DEGraph: differential expression testing for gene networks

Rendered fromDEGraph.Rnwusingutils::Sweaveon Nov 29 2024.

Last update: 2013-11-01
Started: 2013-11-01

Readme and manuals

Help Manual

Help pageTopics
Performs the Adaptive Neyman test of Fan and Lin (1998)AN.test
Annotation data used in the DEGraph package vignetteannLoi2008
Performs the test of Bai and Saranadasa (1996)BS.test
Tamoxifen treatment resistance status data used in the DEGraph package vignetteclassLoi2008
Gene expression data used in the DEGraph package vignetteexprLoi2008
Given a graph, returns a list of its connected components (which are also graph objects), ordered by decreasing number of nodesgetConnectedComponentList
Builds a graph for each of the KEGG pathwaysgetKEGGPathways
Given a graph, builds a signed version of the adjacency matrix taking into account the type of interaction (e.g., activation or inhibition)getSignedGraph
Performs the Hotelling T2 test in Fourier spacegraph.T2.test
KEGG networks used in the DEGraph package vignettegrListKEGG
Performs an hypergeometric test of enrichment of a set of hypotheses in significant elementshyper.test
Calculates the Laplacian associated to an adjacency matrixlaplacianFromA
Plots a graph with nodes colored according to a quantitative variableplotValuedGraph
Generates a random graphrandomWAMGraph
Applies a series of two-sample tests to a connected graph using various statisticstestOneConnectedComponent
Applies a serie of two-sample tests to each connected component of a graph using various statisticstestOneGraph
Given a basis (typically the eigenvectors of a graph Laplacian), builds two multivariate normal samples with mean shift located in the first elements of the basistwoSampleFromGraph
Writes an adjacency matrix into an XML filewriteAdjacencyMatrix2KGML