recutConsensusTrees: Repeat blockwise consensus module detection from pre-calculated data

Description

Given consensus networks constructed for example using blockwiseConsensusModules, 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

recutConsensusTrees(
  multiExpr,
  goodSamples, goodGenes,
  blocks,
  TOMFiles,
  dendrograms,
  corType = "pearson",
  networkType = "unsigned",
  deepSplit = 2,
  detectCutHeight = 0.995, minModuleSize = 20,
  checkMinModuleSize = TRUE,
  maxCoreScatter = NULL, minGap = NULL,
  maxAbsCoreScatter = NULL, minAbsGap = NULL,
  minSplitHeight = NULL, minAbsSplitHeight = NULL,

  useBranchEigennodeDissim = FALSE,
  minBranchEigennodeDissim = mergeCutHeight,

  pamStage = TRUE, pamRespectsDendro = TRUE,
  trimmingConsensusQuantile = 0,
  minCoreKME = 0.5, minCoreKMESize = minModuleSize/3,
  minKMEtoStay = 0.2,
  reassignThresholdPS = 1e-4,
  mergeCutHeight = 0.15,
  mergeConsensusQuantile = trimmingConsensusQuantile,
  impute = TRUE,
  trapErrors = FALSE,
  numericLabels = FALSE,
  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

goodSamples

a list with one component per set. Each component is a logical vector specifying which samples are considered "good" for the analysis. See goodSamplesGenesMS.

goodGenes

a logical vector with length equal number of genes in multiExpr that specifies which genes are considered "good" for the analysis. See goodSamplesGenesMS.

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 t

networkType

network type. Allowed values are (unique abbreviations of) "unsigned", "signed", "signed hybrid". See adjacency. Note that while no new networks are computed in th

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

detectCutHeight

dendrogram cut height for module detection. See cutreeDynamic for more details.

minModuleSize

minimum module size for module detection. See cutreeDynamic for more details.

checkMinModuleSize

logical: should sanity checks be performed on minModuleSize?

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

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 minAbsSpli

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

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 in

trimmingConsensusQuantile

a number between 0 and 1 specifying the consensus quantile used for kME calculation that determines module trimming according to the arguments below.

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 wa

minCoreKMESize

see minCoreKME above.

minKMEtoStay

genes whose eigengene connectivity to their module eigengene is lower than minKMEtoStay are removed from the module.

reassignThresholdPS

per-set p-value ratio threshold for reassigning genes between modules. See Details.

mergeCutHeight

dendrogram cut height for module merging.

mergeConsensusQuantile

consensus quantile for module merging. See mergeCloseModules for details.

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.

Value

A list with the following components:
colorsmodule assignment of all input genes. A vector containing either character strings with module colors (if input numericLabels was unset) or numeric module labels (if numericLabels was set to TRUE). The color "grey" and the numeric label 0 are reserved for unassigned genes.
unmergedColorsmodule colors or numeric labels before the module merging step.
multiMEsmodule eigengenes corresponding to the modules returned in colors, in multi-set format. A vector of lists, one per set, containing eigengenes, proportion of variance explained and other information. See multiSetMEs for a detailed description.

Details

For details on blockwise consensus module detection, see blockwiseConsensusModules. This function implements the module detection subset of the functionality of blockwiseConsensusModules; 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 blockwiseConsensusModules. Working block by block, modules are identified in the dendrograms by the Dynamic Hybrid tree cut. Found modules are trimmed of genes whose consensus module membership kME (that is, correlation with module eigengene) is less than minKMEtoStay. Modules in which fewer than minCoreKMESize genes have consensus 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 (in every set), 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.

References

Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between co-expression modules. BMC Systems Biology 2007, 1:54