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can I use cytoscape for genie3 result?

Hi

I wonder if i could use cytoscape for genie3(gene regulatory network) result for visualization of the interaction among the input genes.

regards

EDIT:

Thanks Leite

The result file was in the csv format, the major challenge for me was the selection of the best informative interaction (or the gene with highest number of interaction )as the file include 19k interaction when run cytoscape the interaction ////////////////////

This is the codes that I run. Is there any parameter that I should include 

library(GENIE3)
library(igraph)
library(RCy3)
library(Rgraphviz)

expr_data <- as.matrix(read.table("upanddowngene.csv", header = T, sep = ",",row.names = 1))
set.seed(expr_data)

weightMat <- GENIE3(expr_data)
linkList <- getLinkList(weightMat)

edge_listsi <- linkList
Gsi <- graph.data.frame(edge_listsi,directed = F)
Asi <- get.adjacency(Gsi,sparse = F,attr = "weight",type = "both")

g_arasi <- graph.adjacency(Asi,mode = "undirected",weighted = T)
g.cyto <- igraph.to.graphNEL(g_arasi)

cw = createNetworkFromGraph("net", graph=g.cyto)
displayGraph (cw)

Regards

rna-seq rna-seq r
• 3,496 views • link
updated by Biostar Biostar • written by Ali • Ali

What is your genie3 output list ???? If it is a txt file with paired interacting genes and a value for each interaction, the answer is yes.

• 0 views • link
written by Leite Leite

@Ali

To process 19k interactions in cytoscape, you would have to have a super computer. Another point, your network would be unreadable.

You need to put a weight filter for interactions, then import into cytoscape.

Get only the links with a weight higher than some threshold

"Alternatively, a threshold can be set on the weights of the links:"

linkList <- getLinkList(weightMat, threshold=0.1)

I think you can get more information by reading this tutorial:

https://bioconductor.org/packages/devel/bioc/vignettes/GENIE3/inst/doc/GENIE3.html

• 0 views • link
written by Leite Leite

@Leite

Thanks for replying I will try set threshold as you said

Kind regards

• 0 views • link
written by Ali • Ali

1 answer

Thanks Leite

The result file was in the csv format, the major challenge for me was the selection of the best informative interaction (or the gene with highest number of interaction )as the file include 19k interaction when run cytoscape the interaction ////////////////////

This is the codes that I run. Is there any parameter that I should include 

library(GENIE3)

library(igraph)

library(RCy3)

library(Rgraphviz)

expr_data <- as.matrix(read.table("upanddowngene.csv", header = T, sep = ",",row.names = 1))

set.seed(expr_data)

weightMat <- GENIE3(expr_data)

linkList <- getLinkList(weightMat)

linkList

edge_listsi <- linkList

Gsi <- graph.data.frame(edge_listsi,directed = F)

Asi <- get.adjacency(Gsi,sparse = F,attr = "weight",type = "both")

g_arasi <- graph.adjacency(Asi,mode = "undirected",weighted = T)

g.cyto <- igraph.to.graphNEL(g_arasi)

cw = createNetworkFromGraph("net", graph=g.cyto)

displayGraph (cw)

Regards

• 0 views • link
updated by zx8754 zx8754 • written by Ali • Ali

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