Nothing

```
#Ac3net:
#This R package allows inferring directional conservative causal core network from large scale data.
#The inferred network consists of only direct physical interactions.
## Copyright (C) January 2011 Gokmen Altay <altayscience@gmail.com>
## This program is a free software for only academic useage but not for commercial useage; you can redistribute it and/or
## modify it under the terms of the GNU GENERAL PUBLIC LICENSE
## either version 3 of the License, or any later version.
##
## This program is distributed WITHOUT ANY WARRANTY;
## You can get a copy of the GNU GENERAL PUBLIC LICENSE
## from
## http://www.gnu.org/licenses/gpl.html
## See the licence information for the dependent package from
## igraph package itself.
#takes an adjacency matrix and returns the absolute maximum correlated partner of each variable on the rows.
Ac3net <- function(DataOrMim, processed=FALSE, ratio_ = 0.002, PCmincutoff=0.6,PCmaxcutoff=0.96, cutoff=0,
estmethod='pearson', pval=1, iterations=10, MTC=FALSE, MTCmethod="BH" )
{ print("DataOrMim can be either data or adjancency matrix.
If you are making a comparison study and already have the adjancency matrix and
eliminated the insignificant scores by you cutoff, then input that matrix to DataOrMim object
and set processed=TRUE. Otherwise just enter your data along with your arbitrary parameter settings.")
if(processed==FALSE) {
DataOrMim <- DataOrMim +1 #make sure no zero
varofCountRows <- apply(DataOrMim, 1, var)
i <- which(varofCountRows==0) # because 0 var gives NA in the mim!
if(sum(i)>0) DataOrMim<- DataOrMim[-i,]
mim <- cor(t(DataOrMim), method = estmethod) #pearson or spearman if unnormalized data
diag(mim) <-0 #no self links allowed
if(MTC==TRUE) {
if(pval==1) {
if(cutoff==0) cutoff <- Ac3net.cutoff(mim, ratio_ = ratio_, PCmincutoff=PCmincutoff, PCmaxcutoff=PCmaxcutoff)
mim[abs(mim) < cutoff] <- 0
}
if(pval < 1){
mimp <- Ac3net.MTC(data=DataOrMim, iterations=iterations, MTC=MTC, MTCmethod=MTCmethod, estmethod=estmethod)
mim[mimp >= pval] <- 0
}
}else{
if(cutoff!=0) mim[abs(mim) < cutoff] <- 0
else{
cutoff <- Ac3net.cutoff(mim=mim, ratio_ = ratio_, PCmincutoff=PCmincutoff, PCmaxcutoff=PCmaxcutoff)
mim[abs(mim) < cutoff] <- 0
}
}
mim <- Ac3net.filtersames(mim)
mim <- Ac3net.maxmim(mim) #returns Ac3net network
}
if(processed==TRUE){#means it is mim matrix and already eliminated by a cutoff
# mim (DataOrMim object), must be filtered (processed) by a cutoff.
mim <- Ac3net.filtersames(mim=DataOrMim)
mim <- Ac3net.maxmim(mim_=mim) #returns Ac3net network
}
return(mim) #returns Ac3net network
}
```

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