DoExpMod {FEM} | R Documentation |
This function aims to identify subnetworks where many members exhibit differential mRNA expression in relation to the phenotype of interest.
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")