DoExpMod: Identifies differential mRNA expression network hotspots.
In FEM: Identification of Functional Epigenetic Modules

Description Usage Arguments Value Author(s) References Examples

This function aims to identify subnetworks where many members exhibit differential mRNA expression in relation to the phenotype of interest.

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

`intExp.o`	The output of the DoIntExp 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
`nd`	smaller than the maximum specified in sizeR.v
`fem`	A summary matrix of the selected modules.
`topmod`	A list of summary matrices for each of the selected module
`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);
intExp.o <- list(statR=Toydata$statR,adj=Toydata$adj);
ExpMod.o=DoExpMod(intExp.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 which contains RNA expression of 17
#normal and 118 endometrial cancer samples. Since running on the realdata is time-consuming,  we comment it out.   
#data(Realdata);
#intExp.o <- list(statM=Realdata$statR,adj=Realdata$adjacency);
#EpiMod.o=DoEpiMod(intExp.o,nseeds=100,gamma=0.5,nMC=1000,sizeR.v=c(1,100),nameSTUDY="TEST")