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22 blockwiseModules minCoreKMESize see minCoreKME above. minKMEtoStay genes whose eigengene connectivity to their module eigengene is lower than minKMEtoStay are removed from the module. reassignThreshold p-value ratio threshold for reassigning genes between modules. See Details. mergeCutHeight dendrogram cut height for module merging. impute logical: should imputation be used for module eigengene calculation See moduleEigengenes for more details. getTOMs deprecated, please use saveTOMs below. saveTOMs logical: should the consensus topological overlap matrices for each block be saved and returned saveTOMFileBase character string containing the file name base for files containing the consensus topological overlaps. The full file names have "block.1.RData", "block.2.RData" etc. appended. These files are standard R data files and can be loaded using the load function. trapErrors logical: should errors in calculations be trapped numericLabels logical: should the returned modules be labeled by colors (FALSE), or by numbers (TRUE) checkMissingData logical: should data be checked for excessive numbers of missing entries in genes and samples, and for genes with zero variance See details. Details quickCor nThreads verbose indent real number between 0 and 1 that controls the handling of missing data in the calculation of correlations. See details. non-negative integer specifying the number of parallel threads to be used by certain parts of correlation calculations. This option only has an effect on systems on which a POSIX thread library is available (which currently includes Linux and Mac OSX, but excludes Windows). If zero, the number of online processors will be used if it can be determined dynamically, otherwise correlation calculations will use 2 threads. integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose. indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces. Before module detection starts, genes and samples are optionally checked for the presence of NAs. Genes and/or samples that have too many NAs are flagged as bad and removed from the analysis; bad genes will be automatically labeled as unassigned, while the returned eigengenes will have NA entries for all bad samples. If blocks is not given and the number of genes exceeds \maxBlockSize, genes are preclustered into blocks using the function projectiveKMeans; otherwise all genes are treated in a single block. For each block of genes, the network is constructed and (if requested) topological overlap is calculated. If requested, the topological overlaps are returned as part of the return value list. Genes
lockwiseModules 23 are then clustered using average linkage hierarchical clustering and modules are identified in the resulting dendrogram by the Dynamic Hybrid tree cut. Found modules are trimmed of genes whose correlation with module eigengene (KME) is less than minKMEtoStay. Modules in which fewer than minCoreKMESize genes have KME higher than minCoreKME are disbanded, i.e., their constituent genes are pronounced unassigned. Conversely, any unassigned genes with KME higher than minKMEtoJoin are automatically assigned to their nearest module. After all blocks have been processed, the function checks whether there are genes whose KME in the module they assigned is lower than KME to another module. If p-values of the higher correlations are smaller than those of the native module by the factor reassignThresholdPS, the gene is re-assigned to the closer module. In the last step, modules whose eigengenes are highly correlated are merged. This is achieved by clustering module eigengenes using the dissimilarity given by one minus their correlation, cutting the dendrogram at the height mergeCutHeight and merging all modules on each branch. The process is iterated until no modules are merged. See mergeCloseModules for more details on module merging. The argument quick specifies the precision of handling of missing data in the correlation calculations. Zero will cause all calculations to be executed precisely, which may be significantly slower than calculations without missing data. Progressively higher values will speed up the calculations but introduce progressively larger errors. Without missing data, all column means and variances can be pre-calculated before the covariances are calculated. When missing data are present, exact calculations require the column means and variances to be calculated for each covariance. The approximate calculation uses the pre-calculated mean and variance and simply ignores missing data in the covariance calculation. If the number of missing data is high, the pre-calculated means and variances may be very different from the actual ones, thus potentially introducing large errors. The quick value times the number of rows specifies the maximum difference in the number of missing entries for mean and variance calculations on the one hand and covariance on the other hand that will be tolerated before a recalculation is triggered. The hope is that if only a few missing data are treated approximately, the error introduced will be small but the potential speedup can be significant. Value A list with the following components: colors a vector of color or numeric module labels for all genes. unmergedColors a vector of color or numeric module labels for all genes before module merging. MEs goodSamples goodGenes dendrograms TOMFiles blockGenes a data frame containing module eigengenes of the found modules (given by colors). numeric vector giving indices of good samples, that is samples that do not have too many missing entries. numeric vector giving indices of good genes, that is genes that do not have too many missing entries. a list whose components conatain hierarchical clustering dendrograms of genes in each block. if saveTOMs==TRUE, a vector of character strings, one string per block, giving the file names of files (relative to current directory) in which blockwise topological overlaps were saved. a list whose components give the indices of genes in each block.
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 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: lockwiseModules 21 randomSeed corTy
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
Page 69 and 70: moduleEigengenes 69 averageExpr pre
Page 71 and 72: multiSetMEs 71 multiSetMEs Calculat
Page 73 and 74:
multiSetMEs 73 Value From the user
Page 75 and 76:
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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