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Package ‘WGCNA’March 26, 2012Ve
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WGCNA-package 5WGCNA-packageWeighte
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WGCNA-package 7cutreeStaticColor Co
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WGCNA-package 9collection of eigeng
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addErrorBars 11Valueno.true negativ
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addGuideLines 13addGuideLinesAdd ve
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adjacency 15DetailsValueNoteselectC
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adjacency.splineReg 17ValueAn adjac
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AFcorMI 19Examples#Simulate a data
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allowWGCNAThreads 21allowWGCNAThrea
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automaticNetworkScreeningGS 23autom
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icor 25pearsonFallbackSpecifies whe
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icorAndPvalue 27DetailsThe function
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lockwiseConsensusModules 29# Consen
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lockwiseConsensusModules 31pearsonF
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lockwiseConsensusModules 33minKMEto
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lockwiseConsensusModules 35NoteTOMF
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lockwiseIndividualTOMs 37nThreads =
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lockwiseIndividualTOMs 39Valuewithi
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lockwiseModules 41pearsonFallback =
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lockwiseModules 43maxPOutliers only
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BloodLists 45MEsOKlogical indicatin
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checkSets 47ArgumentsadjMatsimilari
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chooseTopHubInEachModule 49ValueBot
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coClustering 51coClusteringCo-clust
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coClustering.permutationTest 53verb
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collapseRows 55rowIDcharacter vecto
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collapseRows 57selectedRowis a logi
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collapseRowsUsingKME 59ArgumentsMMG
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conformityBasedNetworkConcepts 61co
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conformityDecomposition 63Descripti
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consensusDissTOMandTree 65A=matrix(
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consensusKME 67UsageconsensusKME(mu
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consensusKME 69meta.Z.RootDoFWeight
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consensusMEDissimilarity 71Z.kME1.S
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consensusProjectiveKMeans 73Details
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cor 75unmergedClustersa numerical v
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cor 77NoteThe implementation uses t
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corPredictionSuccess 79ReferencesPe
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corPvalueStudent 81corPvalueStudent
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coxRegressionResiduals 83DetailsVal
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cutreeStaticColor 85cutreeStaticCol
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exportNetworkToCytoscape 87Author(s
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fixDataStructure 89ValueA data fram
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GOenrichmentAnalysis 91MARDensitya
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GOenrichmentAnalysis 93backgroundTy
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goodGenes 95UsagegoodGenes(datExpr,
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goodSamples 97ValueA logical vector
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goodSamplesGenesMS 99DetailsThis fu
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greenBlackRed 101ArgumentsmultiExpr
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GTOMdist 103ValueA vector of colors
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Inline display of progress 105Argum
- Page 107 and 108: keepCommonProbes 107Author(s)Steve
- Page 109 and 110: labeledBarplot 109fileNameName of t
- Page 111 and 112: labeledHeatmap 111UsagelabeledHeatm
- Page 113 and 114: labeledHeatmap 113Examples# This ex
- Page 115 and 116: labels2colors 115ValueNone.Author(s
- Page 117 and 118: matchLabels 117ValueA symmetric mat
- Page 119 and 120: mergeCloseModules 119maxpowerdiagEn
- Page 121 and 122: mergeCloseModules 121DetailsValueus
- Page 123 and 124: metaAnalysis 123corFnccorOptionsCor
- Page 125 and 126: metaAnalysis 125qValueExtremeScale.
- Page 127 and 128: metaZfunction 127metaZfunctionMeta-
- Page 129 and 130: moduleEigengenes 129Argumentsexprco
- Page 131 and 132: moduleMergeUsingKME 131allPCisPCisH
- Page 133 and 134: moduleNumber 133set.seed(100)MEturq
- Page 135 and 136: modulePreservation 135savePermutedS
- Page 137 and 138: modulePreservation 137DetailsValueT
- Page 139 and 140: multiData.eigengeneSignificance 139
- Page 141 and 142: multiSetMEs 141universalColorsAlter
- Page 143 and 144: mutualInfoAdjacency 143allOKallPCis
- Page 145 and 146: mutualInfoAdjacency 145tual informa
- Page 147 and 148: nearestCentroidPredictor 147Usagene
- Page 149 and 150: nearestCentroidPredictor 149verbose
- Page 151 and 152: nearestNeighborConnectivity 151Argu
- Page 153 and 154: networkConcepts 153ValueA matrix in
- Page 155 and 156: networkConcepts 155Eigengenethe fir
- Page 157: networkScreeningGS 157blockSizebloc
- Page 161 and 162: orderBranchesUsingHubGenes 161Value
- Page 163 and 164: orderMEs 163colorh2 [selectBranch(t
- Page 165 and 166: overlapTableUsingKME 165ValueA list
- Page 167 and 168: pickHardThreshold 167results = over
- Page 169 and 170: pickSoftThreshold 169pickSoftThresh
- Page 171 and 172: plotClusterTreeSamples 171plotClust
- Page 173 and 174: plotColorUnderTree 173plotColorUnde
- Page 175 and 176: plotDendroAndColors 175titlecharact
- Page 177 and 178: plotEigengeneNetworks 177DetailsVal
- Page 179 and 180: plotEigengeneNetworks 179cex.preser
- Page 181 and 182: plotMEpairs 181plotMEpairsPairwise
- Page 183 and 184: plotNetworkHeatmap 183See Alsobarpl
- Page 185 and 186: populationMeansInAdmixture 185Detai
- Page 187 and 188: pquantile 187Argumentsprob A number
- Page 189 and 190: preservationNetworkConnectivity 189
- Page 191 and 192: projectiveKMeans 191Usageprojective
- Page 193 and 194: proportionsInAdmixture 193calculate
- Page 195 and 196: qvalue 195DetailsValueFor compatibi
- Page 197 and 198: qvalue.restricted 197qvalue.restric
- Page 199 and 200: ankPvalue 199DetailsValueThe functi
- Page 201 and 202: ecutBlockwiseTrees 201UsagerecutBlo
- Page 203 and 204: ecutBlockwiseTrees 203DetailsValueF
- Page 205 and 206: ecutConsensusTrees 205TOMFilesdendr
- Page 207 and 208: edWhiteGreen 207NoteunmergedColorsm
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emoveGreyME 209removeGreyMERemoves
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scaleFreeFitIndex 211scaleFreeFitIn
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setCorrelationPreservation 213Forma
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signedKME 215ReferencesBin Zhang an
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simulateDatExpr 217UsagesimulateDat
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simulateDatExpr5Modules 219datExprs
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simulateEigengeneNetwork 221simulat
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simulateMultiExpr 223ValueIf signed
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simulateMultiExpr 225submoduleSpaci
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sizeGrWindow 227DetailsModule eigen
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spaste 229verboseindentinteger leve
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standardScreeningBinaryTrait 231Usa
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standardScreeningCensoredTime 233#
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standardScreeningNumericTrait 235pV
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stat.diag.da 237Author(s)Sandrine D
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stratifiedBarplot 239ArgumentsexpAl
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swapTwoBranches 241corOptionsnetwor
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TOMplot 243sizeGrWindow(12,9);# par
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TOMsimilarityFromExpr 245DetailsThe
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TrueTrait 247indentindentation for
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TrueTrait 249divides by the slope o
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userListEnrichment 251powercorFncco
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userListEnrichment 253matter what i
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userListEnrichment 255MouseMeta ==>
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vectorTOM 257UsagevectorTOM(datExpr
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verboseBarplot 259DetailsThis funct
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verboseIplot 261verboseIplotScatter
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verboseScatterplot 263Argumentsxysa
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votingLinearPredictor 265priorWeigh
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Index∗Topic \textasciitildekwd1ov
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INDEX 269verboseBarplot, 258verbose
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INDEX 271pmin, 187poly, 17populatio