Identifies interactome hotspots of differential promoter DNAm methylation and differential mRNA expression.

Description

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.

Usage

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

Arguments

intFEM.o

The output of the DoFEMbi 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

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

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

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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");