WGCNA: recutBlockwiseTrees – R documentation

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recutBlockwiseTrees

Repeat blockwise module detection from pre-calculated data

Description

Given consensus networks constructed for example using blockwiseModules, this function (re-)detects modules in them by branch cutting of the corresponding dendrograms. If repeated branch cuts of the same gene network dendrograms are desired, this function can save substantial time by re-using already calculated networks and dendrograms.

Usage

recutBlockwiseTrees(
  datExpr,
  goodSamples, goodGenes,
  blocks,
  TOMFiles,
  dendrograms,
  corType = "pearson",
  networkType = "unsigned",
  deepSplit = 2,
  detectCutHeight = 0.995, minModuleSize = min(20, ncol(datExpr)/2 ),
  maxCoreScatter = NULL, minGap = NULL,
  maxAbsCoreScatter = NULL, minAbsGap = NULL,
  minSplitHeight = NULL, minAbsSplitHeight = NULL,

  useBranchEigennodeDissim = FALSE,
  minBranchEigennodeDissim = mergeCutHeight,

  pamStage = TRUE, pamRespectsDendro = TRUE,
  minCoreKME = 0.5, minCoreKMESize = minModuleSize/3,
  minKMEtoStay = 0.3,
  reassignThreshold = 1e-6,
  mergeCutHeight = 0.15, impute = TRUE,
  trapErrors = FALSE, numericLabels = FALSE,
  verbose = 0, indent = 0,
  ...)

Arguments

`datExpr`	expression data. A data frame in which columns are genes and rows ar samples. NAs are allowed, but not too many.
`goodSamples`	a logical vector specifying which samples are considered "good" for the analysis. See `goodSamplesGenes`.
`goodGenes`	a logical vector with length equal number of genes in `multiExpr` that specifies which genes are considered "good" for the analysis. See `goodSamplesGenes`.
`blocks`	specification of blocks in which hierarchical clustering and module detection should be performed. A numeric vector with one entry per gene of `multiExpr` giving the number of the block to which the corresponding gene belongs.
`TOMFiles`	a vector of character strings specifying file names in which the block-wise topological overlaps are saved.
`dendrograms`	a list of length equal the number of blocks, in which each component is a hierarchical clustering dendrograms of the genes that belong to the block.
`corType`	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.
`networkType`	network type. Allowed values are (unique abbreviations of) `"unsigned"`, `"signed"`, `"signed hybrid"`. See `adjacency`.
`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.
`minModuleSize`	minimum module size for module detection. See `cutreeDynamic` for more details.
`maxCoreScatter`	maximum scatter of the core for a branch to be a cluster, given as the fraction of `cutHeight` relative to the 5th percentile of joining heights. See `cutreeDynamic` for more details.
`minGap`	minimum cluster gap given as the fraction of the difference between `cutHeight` and the 5th percentile of joining heights. See `cutreeDynamic` for more details.
`maxAbsCoreScatter`	maximum scatter of the core for a branch to be a cluster given as absolute heights. If given, overrides `maxCoreScatter`. See `cutreeDynamic` for more details.
`minAbsGap`	minimum cluster gap given as absolute height difference. If given, overrides `minGap`. See `cutreeDynamic` for more details.
`minSplitHeight`	Minimum split height given as the fraction of the difference between `cutHeight` and the 5th percentile of joining heights. Branches merging below this height will automatically be merged. Defaults to zero but is used only if `minAbsSplitHeight` below is `NULL`.
`minAbsSplitHeight`	Minimum split height given as an absolute height. Branches merging below this height will automatically be merged. If not given (default), will be determined from `minSplitHeight` above.
`useBranchEigennodeDissim`	Logical: should branch eigennode (eigengene) dissimilarity be considered when merging branches in Dynamic Tree Cut?
`minBranchEigennodeDissim`	Minimum consensus branch eigennode (eigengene) dissimilarity for branches to be considerd separate. The branch eigennode dissimilarity in individual sets is simly 1-correlation of the eigennodes; the consensus is defined as quantile with probability `consensusQuantile`.
`pamStage`	logical. If TRUE, the second (PAM-like) stage of module detection will be performed. See `cutreeDynamic` for more details.
`pamRespectsDendro`	Logical, only used when `pamStage` is `TRUE`. If `TRUE`, the PAM stage will respect the dendrogram in the sense an object can be PAM-assigned only to clusters that lie below it on the branch that the object is merged into. See `cutreeDynamic` for more details.
`minCoreKME`	a number between 0 and 1. If a detected module does not have at least `minModuleKMESize` genes with eigengene connectivity at least `minCoreKME`, the module is disbanded (its genes are unlabeled and returned to the pool of genes waiting for mofule detection).
`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.
`trapErrors`	logical: should errors in calculations be trapped?
`numericLabels`	logical: should the returned modules be labeled by colors (`FALSE`), or by numbers (`TRUE`)?
`verbose`	integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose.
`indent`	indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.
`...`	Other arguments.

Details

For details on blockwise module detection, see blockwiseModules. This function implements the module detection subset of the functionality of blockwiseModules; network construction and clustering must be performed in advance. The primary use of this function is to experiment with module detection settings without having to re-execute long network and clustering calculations whose results are not affected by the cutting parameters.

This function takes as input the networks and dendrograms that are produced by blockwiseModules. Working block by block, modules are identified in the dendrogram by the Dynamic Hybrid Tree Cut algorithm. 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.

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.

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`	a data frame containing module eigengenes of the found modules (given by `colors`).
`MEsOK`	logical indicating whether the module eigengenes were calculated without errors.

Author(s)

Peter Langfelder

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