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14 blockwiseConsensusModules blockwiseConsensusModules Find consensus modules across several datasets. Description Usage Perform network construction and consensus module detection across several datasets. blockwiseConsensusModules( multiExpr, blocks = NULL, maxBlockSize = 5000, randomSeed = 12345, corType = "pearson", power = 6, consensusQuantile = 0, networkType = "unsigned", TOMType = "unsigned", scaleTOMs = TRUE, scaleQuantile = 0.95, sampleForScaling = TRUE, sampleForScalingFactor = 1000, useDiskCache = TRUE, chunkSize = NULL, cacheBase = ".blockConsModsCache", deepSplit = 2, detectCutHeight = 0.995, minModuleSize = 20, checkMinModuleSize = TRUE, maxCoreScatter = NULL, minGap = NULL, maxAbsCoreScatter = NULL, minAbsGap = NULL, pamStage = TRUE, pamRespectsDendro = TRUE, minKMEtoJoin =0.7, minCoreKME = 0.5, minCoreKMESize = minModuleSize/3, minKMEtoStay = 0.2, reassignThresholdPS = 1e-4, mergeCutHeight = 0.15, impute = TRUE, getTOMs = NULL, saveTOMs = FALSE, saveTOMFileBase = "consensusTOM", getTOMScalingSamples = FALSE, trapErrors = FALSE, checkPower = TRUE, numericLabels = FALSE, checkMissingData = TRUE, quickCor = 0, nThreads = 0, verbose = 2, indent = 0) Arguments multiExpr expression data in the multi-set format (see checkSets). A vector of lists, one per set. Each set must contain a component data that contains the expression data, with rows corresponding to samples and columns to genes or probes.
lockwiseConsensusModules 15 blocks optional specification of blocks in which hierarchical clustering and module detection should be performed. If given, must be a numeric vector with one entry per gene of multiExpr giving the number of the block to which the corresponding gene belongs. maxBlockSize integer giving maximum block size for module detection. Ignored if blocks above is non-NULL. Otherwise, if the number of genes in datExpr exceeds maxBlockSize, genes will be pre-clustered into blocks whose size should not exceed maxBlockSize. randomSeed corType integer to be used as seed for the random number generator before the function starts. If a current seed exists, it is saved and restored upon exit. If NULL is given, the function will not save and restore the seed. character string specifying the correlation to be used. Allowed values are (unique abbreviations of) "pearson" and "bicor", corresponding to Pearson and bidweight midcorrelation, respectively. Missing values are handled using the pariwise.complete.obs option. power soft-thresholding power for netwoek construction. consensusQuantile qunatile at which consensus is to be defined. See details. networkType network type. Allowed values are (unique abbreviations of) "unsigned", "signed", "signed hybrid". See adjacency. TOMType one of "none", "unsigned", "signed". If "none", adjacency will be used for clustering. If "unsigned", the standard TOM will be used (more generally, TOM function will receive the adjacency as input). If "signed", TOM will keep track of the sign of correlations between neighbors. scaleTOMs should set-specific TOM matrices be scaled to the same scale scaleQuantile if scaleTOMs is TRUE, topological overlaps (or adjacencies if TOMs are not computed) will be scaled such that their scaleQuantile quantiles will agree. sampleForScaling if TRUE, scale quantiles will be determined from a sample of network similarities. Note that using all data can double the memory footprint of the function and the function may fail. sampleForScalingFactor determines the number of samples for scaling: the number is 1/scaleQuantile * sampleForScalingFactor. Should be set well above 1 to ensure accuracy of the sampled quantile. useDiskCache should calculated network similarities in individual sets be temporarilly saved to disk Saving to disk is somewhat slower than keeping all data in memory, but for large blocks and/or many sets the memory footprint may be too big. chunkSize cacheBase network similarities are saved in smaller chunks of size chunkSize. character string containing the desired name for the cache files. The actual file names will consists of cacheBase and a suffix to make the file names unique. deepSplit integer value between 0 and 4. Provides a simplified control over how sensitive module detection should be to module splitting, with 0 least and 4 most sensitive. See cutreeDynamic for more details. detectCutHeight dendrogram cut height for module detection. See cutreeDynamic for more details.
Page 1 and 2: Package ‘WGCNA’ March 17, 2009
Page 3 and 4: R topics documented: 3 plotClusterT
Page 5 and 6: addGuideLines 5 Details Note If lin
Page 7 and 8: adjacency 7 Arguments datExpr selec
Page 9 and 10: automaticNetworkScreeningGS 9 Argum
Page 11 and 12: 0.1. WARNING 11 0.1 Warning .... Au
Page 13: icor 13 nThreads verbose indent non
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
Page 57 and 58: keepCommonProbes 57 Author(s) Steve
Page 59 and 60: labeledHeatmap 59 Value None. Autho
Page 61 and 62: labeledHeatmap 61 Examples # This e
Page 63 and 64: matchLabels 63 Examples labels = c(
Page 65 and 66:
mergeCloseModules 65 Details Value
Page 67 and 68:
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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