require("devtools") devtools::install_github("raywoo32/modCluster", dependencies=TRUE) library("modCluster")
For graphs with biological significance it is common for there to be "modules" associated with verticies. Some examples include sub-cellular location, co-expression module or GO-term. Modcluster stands for "cluster by module", and groups modules together if they have an increased edge density together. With this package biologists will be able to get an idea which modules share highly significant communication through edges.
This project was created following instruction provided by BCB410 at the University of Toronto. Important packages to its creation include igraph, knittr, mclust (documentation examples), Hadley Wickham's Rpackages, shiny and Boris Steipe's rpt package. For full citations please see README.Rmd.
Very simply here follows a description of how modCluster operates
A simple example of how to use modCluster
library(modCluster) clusterByModule(edgesData, verticiesData, displayCommunity=FALSE) clusterByModule(edgesData, verticiesData, displayCommunity=TRUE)
modCluster only exports 1 function:
| GENE1 | GENE2 | WEIGHT | |-------|------ |--------| | G1 | G2 | 1 |
| GENE | MODULE |
|------|--------|
| G1 | 1 |
| G2 | 2 |
displayCommunity a boolean flag to choose to display community markers.
For other references please see README.Rmd Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models The R Journal 8/1, pp. 205-233
sessionInfo()
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