Package: netZooR 1.9.0

Marouen Ben Guebila

netZooR: Unified methods for the inference and analysis of gene regulatory networks

netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.

Authors:Marouen Ben Guebila [aut, cre], Tian Wang [aut], John Platig [aut], Marieke Kuijjer [aut], Megha Padi [aut], Rebekka Burkholz [aut], Des Weighill [aut], Kate Shutta [aut]

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netZooR.pdf |netZooR.html✨
netZooR/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/netzoo/netzoor/issues

Datasets:
  • exon.size - Gene length
  • genes - Example of a gene list
  • monsterRes - MONSTER results from example cell-cycle yeast transition
  • mut.ucec - Example of mutation data
  • small1976 - Pollinator-plant interactions
  • yeast - Toy data derived from three gene expression datasets and a mapping from transcription factors to genes.

On BioConductor:netZooR-1.9.0(bioc 3.20)netZooR-1.8.0(bioc 3.19)

bioconductor-package

41 exports 1.58 score 202 dependencies

Last updated 2 months agofrom:39d320e80c

Exports:alpacaalpacaCranealpacaExtractTopGenescondorClustercondorCoreEnrichcondorMatrixModularitycondorModularityMaxcondorPlotCommunitiescondorPlotHeatmapcondorQscorecraneBipartitecraneUnipartitecreateCondorObjectcreatePandaStyledragonelistToAdjMatlionesslionessPymonstermonsterBereFullmonsterCalculateTmPValuesmonsterCheckDataTypemonsterdTFIPlotmonsterGetTmmonsterHclHeatmapPlotmonsterMonsterNImonsterPlotMonsterAnalysismonsterPrintMonsterAnalysismonsterTransformationMatrixmonsterTransitionNetworkPlotmonsterTransitionPCAPlototterpandaDiffEdgespandaPypandaToAlpacapandaToCondorObjectrunEgretsambarsourcePPIspidervisPandaInCytoscape

Dependencies:abindannotateAnnotationDbiAnnotationForgeaskpassassertthatbackportsbase64base64encbase64urlbeanplotBHBiobaseBiocFileCacheBiocGenericsBiocIOBiocParallelbiomaRtBiostringsbitbit64bitopsblobbumphuntercachemCategorycaToolschronclicliprclustercodetoolscolorspacecpp11crayoncurldata.tableDBIdbplyrDelayedArrayDelayedMatrixStatsdigestdoParalleldoRNGdownloaderdplyredgeRevaluatefansifarverfastmapfilelockforeachformatRfsfutile.loggerfutile.optionsgenefiltergenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicFeaturesGenomicRangesGEOqueryggdendroggplot2glueGO.dbGOstatsgplotsgraphGSEABasegsubfngtablegtoolshashHDF5Arrayherehexbinhmshtmltoolshttrhttr2igraphilluminaioIRangesIRdisplayIRkernelisobanditeratorsjsonliteKEGGRESTKernSmoothlabelinglambda.rlatticelifecyclelimmalocfitmagrittrMASSMatrixmatrixcalcMatrixGenericsmatrixStatsmclustmemoisemgcvmimeminfimulttestmunsellnlmennetnor1mixopensslorg.Hs.eg.dbpandaRpbdZMQpenalizedpermutepillarpkgconfigplogrplotrixplyrpngpreprocessCoreprettyunitsprogressprotopurrrquadprogquantroR.methodsS3R.ooR.utilsR6rappdirsRBGLRColorBrewerRcppRcppArmadilloRcppTOMLRCurlRCy3readrreprreshapereshape2restfulrreticulateRgraphvizrhdf5rhdf5filtersRhdf5libRhtslibrjsonRJSONIOrlangrngtoolsrprojrootRsamtoolsRSQLitertracklayerRUnitS4ArraysS4VectorsscalesscrimesiggenessnowSparseArraysparseMatrixStatssqldfstatmodSTRINGdbstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttzdbUCSC.utilsutf8uuidvctrsveganviridisLitevroomwithrXMLxml2xtableXVectoryamlyarnzlibbioc

Using CONDOR for community detection in bipartite graphs

John Platig

Rendered fromCONDOR.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2021-11-14
Started: 2020-12-06

Readme and manuals

Help Manual

Help pageTopics
converts adjacency matrix to edge listadjMatToElist
Main ALPACA functionalpaca
Comparing node community membership between two networksalpacaCommunityStructureRotation
Compute Differential modularity score from differential modularity matrixalpacaComputeDifferentialScoreFromDWBM
Differential modularity matrixalpacaComputeDWBMmatmScale
Compute modularity matrix for weighted bipartite networkalpacaComputeWBMmat
Find the robust nodes in ALPACA community using CRANEalpacaCrane
Edge subtraction method (CONDOR optimizaton)alpacaDeltaZAnalysis
Edge subtraction method (Louvain optimizaton)alpacaDeltaZAnalysisLouvain
Extract core target genes in differential modulesalpacaExtractTopGenes
Generalized Louvain optimizationalpacaGenLouvain
get the member vector from alpaca objectalpacaGetMember
The top GO term associated genes in each modulealpacaGOtabtogenes
Map GO terms to gene symbolsalpacaGoToGenes
GO term enrichment for a list of gene setsalpacaListToGo
Create alpacaMetaNetwork for Louvain optimizationalpacaMetaNetwork
Remove tags from gene namesalpacaNodeToGene
Converts alpaca output into list of data framesalpacaObjectToDfList
Community comparison method (CONDOR optimizaton)alpacaRotationAnalysis
Community comparison method (CONDOR optimizaton)alpacaRotationAnalysisLouvain
Simulated networksalpacaSimulateNetwork
Enrichment in ranked listalpacaTestNodeRank
Renumbering community membership vectoralpacaTidyConfig
Translating gene identifiers to gene symbolsalpacaTopEnsembltoTopSym
Generalized Louvain method for bipartite networksalpacaWBMlouvain
Main clustering function for condor.condorCluster
Compare qscore distribution of a subset of nodes to all other nodes.condorCoreEnrich
creates condor objectcondorCreateObject
Iteratively maximize bipartite modularity.condorMatrixModularity
Iteratively maximize bipartite modularity.condorModularityMax
Plot adjacency matrix with links grouped and colored by communitycondorPlotCommunities
Plot weighted adjacency matrix with links grouped by communitycondorPlotHeatmap
Calculate Qscore for all nodescondorQscore
Run CONDOR clusteringcondorRun
Pertrubs the bipartite network with fixed node strengthcraneBipartite
Pertrubs the unipartite network with fixed node strength from adjacency matrixcraneUnipartite
Create list amenable to analysis using 'condor' package.createCondorObject
Create a Cytoscape visual style for PANDA networkcreatePandaStyle
Function to adjust the degree so that the hub nodes are not penalized in z-score transformationdegreeAdjust
Run DRAGON in R.dragon
Adds "_A" to first column and "_B" to second columnelistAddTags
check if first two columns are identicalelistIsEdgeOrderEqual
undo elistAddTagselistRemoveTags
Sorts the edge list based on first two columns in alphabetical orderelistSort
Converts edge list to adjacency matrixelistToAdjMat
Gene lengthexon.size
Example of a gene listgenes
Check if data frame is an edge listisElist
CRANE Beta perturbation function. This function will add noice to the node strength sequence.jutterDegree
Compute LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples)lioness
Run python implementation of LIONESSlionessPy
MOdeling Network State Transitions from Expression and Regulatory data (MONSTER)monster
Bipartite Edge Reconstruction from Expression data (composite method with direct/indirect)monsterBereFull
Calculate p-values for a tranformation matrixmonsterCalculateTmPValues
Checks that data is something MONSTER can handlemonsterCheckDataType
This function plots the Off diagonal mass of an observed Transition Matrix compared to a set of null TMsmonsterdTFIPlot
monsterGetTmmonsterGetTm
Transformation matrix plotmonsterHclHeatmapPlot
Bipartite Edge Reconstruction from Expression datamonsterMonsterNI
monsterPlotMonsterAnalysismonsterPlotMonsterAnalysis
monsterPrintMonsterAnalysismonsterPrintMonsterAnalysis
MONSTER results from example cell-cycle yeast transitionmonsterRes
Bi-partite network analysis toolsmonsterTransformationMatrix
This function uses igraph to plot the transition matrix (directed graph) as a network. The edges in the network should be read as A 'positively/negatively contributes to' the targeting of B in the target state.monsterTransitionNetworkPlot
Principal Components plot of transformation matrixmonsterTransitionPCAPlot
Example of mutation datamut.ucec
Run OTTER in Rotter
Identify differential edges in two PANDA networkspandaDiffEdges
Run Python implementation PANDA in RpandaPy
Use two PANDA network to generate an ALPACA resultpandaToAlpaca
Turn PANDA network into a CONDOR objectpandaToCondorObject
Run EGRET in RrunEgret
Main SAMBAR function.sambar
Convert .gmt files into a binary matrix.sambarConvertgmt
Normalize gene mutation scores by gene length.sambarCorgenelength
De-sparsify gene-level mutation scores into gene set-level mutation scores.sambarDesparsify
Pollinator-plant interactionssmall1976
Source the Protein-Protein interaction in STRING databasesourcePPI
Seeding PANDA Interactions to Derive Epigenetic Regulationspider
Plot PANDA network in CytoscapevisPandaInCytoscape
Toy data derived from three gene expression datasets and a mapping from transcription factors to genes.yeast