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6 adjacency addTraitToMEs Add trait information to multi-set module eigengene structure Description Adds trait information to multi-set module eigengene structure. Usage addTraitToMEs(multiME, multiTraits) Arguments multiME multiTraits Module eigengenes in multi-set format. A vector of lists, one list per set. Each list must contain an element named data that is a data frame with module eigengenes. Microarray sample trait(s) in multi-set format. A vector of lists, one list per set. Each list must contain an element named data that is a data frame in which each column corresponds to a trait, and each row to an individual sample. Details The function simply cbind’s the module eigengenes and traits for each set. The number of sets and numbers of samples in each set must be consistent between multiMEs and multiTraits. Value A multi-set structure analogous to the input: a vector of lists, one list per set. Each list will contain a component data with the merged eigengenes and traits for the corresponding set. Author(s) Peter Langfelder See Also checkSets, moduleEigengenes adjacency Calculate network adjacency Description Calculates network adjacency from given expression data. Usage adjacency(datExpr, selectCols = NULL, power = 6, type = "unsigned", corFnc = "co
adjacency 7 Arguments datExpr selectCols power data frame containing expression data. Columns correspond to genes and rows to samples. can be used to select genes whose adjacencies will be calculated. Should be either a numeric vector giving the indices of the genes to be used, or a boolean vector indicating which genes are to be used. soft thresholding power. type network type. Allowed values are (unique abbreviations of) "unsigned", "signed", "signed hybrid". corFnc corOptions character string specifying the function to be used to calculate co-expression similarity. Defaults to Pearson correlation. Any function returning values between -1 and 1 can be used. character string specifying additional arguments to be passed to the function given by corFnc. Use "use = ’p’, method = ’Spearman’" to obtain Spearman correlation. Details The function calculates the similarity of columns (genes) in datExpr by calling the function given in corFnc, transforms the similarity according to type and raises it to power, resulting in a weighted network adjacency matrix. If selectCols is given, the corFnc function will be given arguments (datExpr, datExpr[selectCols], ...); hence the returned adjacency will have rows corresponding to all genes and columns corresponding to genes selected by selectCols. Value Adjacency matrix of dimensions nrow(datExpr) times nrow(datExpr). If selectCols was given, the number of columns will be the length (if numeric) or sum (if boolean) of selectCols. Author(s) Peter Langfelder and Steve Horvath References Bin Zhang and Steve Horvath (2005) A General Framework for Weighted Gene Co-Expression Network Analysis, Statistical Applications in Genetics and Molecular Biology, Vol. 4 No. 1, Article 17 Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between coexpression modules. BMC Systems Biology 2007, 1:54
Page 1 and 2: Package ‘WGCNA’ March 17, 2009
Page 3 and 4: R topics documented: 3 plotClusterT
Page 5: addGuideLines 5 Details Note If lin
Page 9 and 10: automaticNetworkScreeningGS 9 Argum
Page 11 and 12: 0.1. WARNING 11 0.1 Warning .... Au
Page 13 and 14: icor 13 nThreads verbose indent non
Page 15 and 16: lockwiseConsensusModules 15 blocks
Page 17 and 18: lockwiseConsensusModules 17 getTOMS
Page 19 and 20: lockwiseConsensusModules 19 blockOr
Page 21 and 22: lockwiseModules 21 randomSeed corTy
Page 23 and 24: lockwiseModules 23 are then cluster
Page 25 and 26: checkSets 25 Arguments adjMat min m
Page 27 and 28: collectGarbage 27 collectGarbage It
Page 29 and 30: consensusOrderMEs 29 consensusOrder
Page 31 and 32: consensusProjectiveKMeans 31 useMea
Page 33 and 34: 0.2. WARNING 33 Author(s) who you a
Page 35 and 36: cor 35 cor Fast calculations of Pea
Page 37 and 38: correlationPreservation 37 data = m
Page 39 and 40: cutreeStatic 39 See Also hclust for
Page 41 and 42: exportNetworkToCytoscape 41 Argumen
Page 43 and 44: fixDataStructure 43 Arguments adjMa
Page 45 and 46: goodGenesMS 45 Arguments multiExpr
Page 47 and 48: goodSamplesGenesMS 47 Author(s) Pet
Page 49 and 50: goodSamplesMS 49 Details This funct
Page 51 and 52: greenBlackRed 51 Arguments datExpr
Page 53 and 54: GTOMdist 53 Author(s) Peter Langfel
Page 55 and 56: Inline display of progress 55 Argum
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keepCommonProbes 57 Author(s) Steve
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labeledHeatmap 59 Value None. Autho
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labeledHeatmap 61 Examples # This e
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matchLabels 63 Examples labels = c(
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mergeCloseModules 65 Details Value
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moduleEigengenes 67 See Also module
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moduleEigengenes 69 averageExpr pre
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multiSetMEs 71 multiSetMEs Calculat
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multiSetMEs 73 Value From the user
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nearestNeighborConnectivity 75 samp
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networkConcepts 77 networkConcepts
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0.3. WARNING 79 Author(s) who you a
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0.4. WARNING 81 Arguments y Details
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0.4. WARNING 83 datMEBatch, use = "
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numbers2colors 85 numbers2colors Co
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overlapTable 87 Value A vector of l
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pickSoftThreshold 89 Author(s) Stev
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plotClusterTreeSamples 91 Usage plo
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plotDendroAndColors 93 Arguments De
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plotEigengeneNetworks 95 Details Va
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plotEigengeneNetworks 97 Details Va
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plotModuleSignificance 99 plotModul
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preservationNetworkConnectivity 101
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projectiveKMeans 103 pairwiseWeight
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propVarExplained 105 propVarExplain
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ecutBlockwiseTrees 107 See Also obj
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ecutBlockwiseTrees 109 minModuleSiz
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ecutConsensusTrees 111 recutConsens
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ecutConsensusTrees 113 Details Valu
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elativeCorPredictionSuccess 115 Val
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emoveGreyME 117 } if (sign1 == 0) s
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setCorrelationPreservation 119 setC
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signedKME 121 signedKME Signed eige
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simulateDatExpr5Modules 123 Usage s
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simulateDatExpr 125 backgroundNoise
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simulateModule 127 Arguments Detail
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simulateMultiExpr 129 See Also simu
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simulateSmallLayer 131 } nSamples =
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sizeGrWindow 133 sizeGrWindow Opens
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standardColors 135 standardColors C
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TOMsimilarityFromExpr 137 See Also
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unsignedAdjacency 139 Value A matri
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verboseBoxplot 141 corOptions type
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verboseScatterplot 143 Examples ##-
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WGCNA-package 145 } y = as.numeric(
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WGCNA-package 147 hubGeneSignifican
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Index ∗Topic kwd1 automaticNetwor
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INDEX 151 greenBlackRed, 48 greenWh
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