coregnet
. This can be used on networks inferred from gene expression data by other algorithms (ARACNe, GENIE3 ...) but also on ChIP-seq data or on network based on Transcription Factor Binding Site analysis. If the input network does not contain a dscription of the type of regulation (activation, inhibition) an expression data set is needed.
coregnet(GRN, expressionDATA = NULL)
coregnet
object.
In order to import a network and initialize a new coregnet object the input data.frame should have three columns, the first containing the target genes, then the activators and finally the inhibitors. Target genes can be present in multiple lines. Several regulators can be present in column 2 and 3 if they are separated by a space. Below is an example of a toy network :
A;R1 R2;R3 R4
A;R1 R5;R3 R6
B;R1 R2 R3;NA
...
hLICORN
acts=apply(matrix(rep(letters[1:4],4),nrow=2),2,paste,collapse=" ")
reps=apply(matrix(rep(letters[5:8],4),nrow=2),2,paste,collapse=" ")
grn=data.frame("Target"= LETTERS[1:16] ,"coact"=c(acts,reps),"corep"= c(reps,acts),"R2"=runif(16),stringsAsFactors=FALSE)
co=coregnet(grn)
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