Rather than assuming that a module is a set of nodes connected to one another, edge clustering defines modules as sets of closely interrelated links.
Finally, modules, or sets of closely linked genes, were identified in each of the networks using the link community method [ 51] which circumscribes edges into communities using the 'linkcomm' binary version 1.0-4 in R [ 57] or MCL modules using the 'mcl' binary version 11-294 (http://micans.org/mcl/).org/mcl/
The detailed view of the relatedness between each pair of neurons shown in Fig. S1 indicates that, while in each module there are subgroups of closely related neurons, different subgroups within the same module may be very far from each other in the lineage tree.
In the map of pathways, the modules of interactive pathways can be detected, where a module represents a group of closely inter-connected pathways.
This led to a total of 21,327 co-expression modules, and as expected, there was some degree of overlap in the composition of closely related modules.
This algorithm performs a hierarchical clustering of interactions, that is, instead of assuming that a functional module is a set of nodes with many interactions between them, it considers a module to be a set of closely interrelated interactions.
The algorithm based on link communities performs a hierarchical clustering of interactions and considers a module to be a set of closely interrelated interactions.
Here we presented a new formulation of the module identification task: a group of closely connected and co-expressed differentially expressed (DE) genes in genome-wide gene networks are regarded as the signatures of the underlying responsive gene modules at the RNA expression level.
The geometric theory of random normed modules is closely related to the geometry of Banach spaces since a complete random normed module is a kind of random generalization of a Banach space, as demonstrated by [17, 18].
