Package 'WGCNA' - Laboratory Web Sites - UCLA

18 blockwiseConsensusModules
blocks
blockOrder
originCount
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.
if the input consensusQuantile==0, this vector will contain counts of how
many times each set contributed the consensus gene similarity value. If the
counts are highly unbalanced, the consensus may be biased.
TOMScalingSamples
if the input getTOMScalingSamples is TRUE, this component is a list with
one component per block. Each component is again a list with two components:
sampleIndex contains indices of the distance structure in which TOM is
stored that were sampled, and TOMSamples is a matrix whose rows correspond
to TOM samples and columns to individual set. Hence, TOMScalingSamples[[blockNo]]$TO
setNo] contains the TOM entry that corresponds to element TOMScalingSamples[[blockNo]
of the TOM distance structure in block blockNo and set setNo. (For details
on the distance structure, see dist.)
Note
If the input datasets have large numbers 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 7000 genes.
Author(s)
Peter Langfelder
References
Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between coexpression
modules. BMC Systems Biology 2007, 1:54
See Also
goodSamplesGenesMS for basic quality control and filtering;
adjacency, TOMsimilarity for network construction;
hclust for hierarchical clustering;
cutreeDynamic for adaptive branch cutting in hierarchical clustering dendrograms;
mergeCloseModules for merging of close modules.