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22 blockwiseModules
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
goodSamples
goodGenes
dendrograms
TOMFiles
blockGenes
blocks
blockOrder
MEsOK
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.
if input blocks was given, its copy; otherwise a vector of length equal number
of genes giving the block label for each gene. Note that block labels are not
necessarilly sorted in the order in which the blocks were processed (since we do
not require this for the input blocks). See blockOrder below.
a vector giving the order in which blocks were processed and in which blockGenes
above is returned. For example, blockOrder[1] contains the label of the
first-processed block.
logical indicating whether the module eigengenes were calculated without errors.
Note
If the input dataset has a large number of genes, consider carefully the maxBlockSize as it significantly
affects the memory footprint (and whether the function will fail with a memory allocation
error). From a theoretical point of view it is advantageous to use blocks as large as possible; on the
other hand, using smaller blocks is substantially faster and often the only way to work with large
numbers of genes. As a rough guide, it is unlikely a standard desktop computer with 4GB memory
or less will be able to work with blocks larger than 8000 genes.
Author(s)
Peter Langfelder