DoFEMbi: Identifies interactome hotspots of differential promoter DNAm...
In FEM: Identification of Functional Epigenetic Modules

Description Usage Arguments Value Author(s) References Examples

DoFEMbi identifies interactome hotspots of differential promoter methylation and differential expression in relation to a phenotype of interest, where an inverse association between methylation and gene expression is assumed.

1 2	DoFEMbi(intFEM.o, nseeds = 100, gamma = 0.5, nMC = 1000, sizeR.v = c(1,100), minsizeOUT = 10, writeOUT = TRUE, nameSTUDY = "X", ew.v = NULL)

`intFEM.o`	The output of the DoIntFEM450k function.
`nseeds`	An integer specifying the number of seeds and therefore modules to search for. By default this number is 100.
`gamma`	A parameter of the spin-glass algorithm, which determines the average module size. Default value generally leads to modules in the desired size range (10-100 genes).
`nMC`	Number of Monte Carlo runs for establishing statistical significance of modularity values under randomisation of the molecular profiles on the network.
`sizeR.v`	Desired size range for modules.
`minsizeOUT`	Minimum size of modules to report as interesting.
`writeOUT`	A logical to indicate whether to write out tables in text format.
`nameSTUDY`	A name for the study, to be used as label in the output files.
`ew.v`	The edge weight vector of the integrated network. This is actually generated by the function itself, and can speed up inference significantly, if provided as argument to a 2nd instance of the function. Default value is NULL.

A list with following entries:

`size`	A vector of inferred module sizes for each of the ntop seeds.
`mod`	A vector of associated modularities.
`pv`	A vector of associated significance P-values with resolution of nMC
`selmod`	Index positions of significant modules of size at least minsizeOUT
`fem`	A summary matrix of the selected modules.
`topmod`	A list of summary matrices for each of the selected modules.
`sgc`	A list of the spin-glass module detection algorithm for each seed.
`ew`	The edge-weight vector of the integrated network.
`adj`	adjacency matrix of the maximally connected integrated network (at present only maximally connected subnetwork is used).It is same to intFEM.o$adj, and wil be used for FemModShow function

"Yinming Jiao"<20907099@zju.edu.cn>, "Andrew E Teschendorff"<andrew@picb.ac.cn>

A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Jiao Y, Widschwendter M, Teschendorff AE. Bioinformatics. 2014;30(16):2360-2366

data(Toydata);
intFEM.o <- list(statM=Toydata$statM,statR=Toydata$statR,adj=Toydata$adj);
DoFEMbi(intFEM.o,nseeds=1,gamma=0.5,nMC=1000,sizeR.v=c(1,100),
 minsizeOUT=10,writeOUT=TRUE,nameSTUDY="TEST",ew.v=NULL);
#You can also test on the Realdata contains matched DNA methylation and RNA Expression of 17 normal and 118 cancer samples. Since running on the realdata is time-consuming, we comment it out.   
#data(Realdata);
#intFEM.o <- list(statM=Realdata$statM,statR=Realdata$statR,adj=Realdata$adjacency);
#DoFEMbi(intFEM.o,nseeds=100,gamma=0.5,nMC=1000,sizeR.v=c(1,100),nameSTUDY="TEST");