DoExpMod {FEM} | R Documentation

## Identifies differential mRNA expression network hotspots.

### Description

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

### Usage

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

### Arguments

`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.

### Value

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

### Author(s)

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

### References

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

### Examples

    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")

[Package FEM version 3.8.0 Index]