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.
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,
...)
expression data. A data frame in which columns are genes and rows ar samples. NAs are allowed, but not too many.
a logical vector specifying
which samples are considered "good" for the analysis. See goodSamplesGenes.
a logical vector with length equal number of genes in multiExpr that
specifies which genes are considered "good" for the analysis. See goodSamplesGenes.
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.
a vector of character strings specifying file names in which the block-wise topological overlaps are saved.
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.
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.
network type. Allowed values are (unique abbreviations of) "unsigned",
"signed", "signed hybrid". See adjacency.
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.
dendrogram cut height for module detection. See
cutreeDynamic for more details.
minimum module size for module detection. See
cutreeDynamic for more details.
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.
minimum cluster gap given as the fraction of the difference between cutHeight and
the 5th percentile of joining heights. See cutreeDynamic for more details.
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.
minimum cluster gap given as absolute height difference. If given, overrides
minGap. See cutreeDynamic for more details.
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.
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.
Logical: should branch eigennode (eigengene) dissimilarity be considered when merging branches in Dynamic Tree Cut?
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.
logical. If TRUE, the second (PAM-like) stage of module detection will be performed.
See cutreeDynamic for more details.
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.
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).
see minCoreKME above.
genes whose eigengene connectivity to their module eigengene is lower than
minKMEtoStay are removed from the module.
p-value ratio threshold for reassigning genes between modules. See Details.
dendrogram cut height for module merging.
logical: should imputation be used for module eigengene calculation? See
moduleEigengenes for more details.
logical: should errors in calculations be trapped?
logical: should the returned modules be labeled by colors (FALSE), or by
numbers (TRUE)?
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.
Other arguments.
A list with the following components:
a vector of color or numeric module labels for all genes.
a vector of color or numeric module labels for all genes before module merging.
a data frame containing module eigengenes of the found modules (given by colors).
logical indicating whether the module eigengenes were calculated without errors.
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.
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
blockwiseModules for full module calculation;
cutreeDynamic for adaptive branch cutting in hierarchical clustering
dendrograms;
mergeCloseModules for merging of close modules.