Loading package and necessary data
library(LEANR) set.seed(123456) # load network and CCM p-values data(g2) data(CCM.pvals) data(gene.annots)
library(LEANR) set.seed(123456) # load network and CCM p-values data(g2) data(CCM.pvals) data(gene.annots) LEAN_results<-LEANR:::LEAN_results
Run LEAN on the precomputed differential expression p-values (computed using limma) in turn for each of the three CCM gene knock-out experiments. As we run these calculations with background distributions of size 10.000, they will take a little time depending on how many cores are available on your machine.
LEAN_results<-lapply(names(CCM.pvals),function(ccm){ run.lean(CCM.pvals[[ccm]], g2, n_reps = 10000, ncores = 3) }) names(LEAN_results)<-names(CCM.pvals)
Extract significant local subnetworks in each of the three experiments and show which local subnetworks are significant in all three knock-out experiments
# Extract significant local subnetworks sign.genes<-lapply(LEAN_results,function(LEANres){ rownames(LEANres$restab[LEANres$restab[,'PLEAN']<=0.05,]) }) names(sign.genes)<-names(CCM.pvals) # Show local subnetworks detected as significant in all three knock-outs all.sign<-intersect(intersect(sign.genes[[1]],sign.genes[[2]]),sign.genes[[3]]) print(gene.annots[all.sign,])
The local subnetwork around VWF is the only one significant in all three knock-outs. Now we extract the local subnetwork around VWF and create a Cytoscape-readable subnetwork file
# Extract information on the detected local subnetwork (around VWF) vwf.ls.info<-get.ls.info(all.sign[1],LEAN_results$CCM2) print(head(vwf.ls.info)) write.table(vwf.ls.info,'./VWF_subnetwork_info_CCM2.txt',sep='\t',quote=F,row.names=F) # Create sif file representing the local subnetwork around VWF write.ls.to.sif(all.sign[1],LEAN_results$CCM2,'./VWF_subnetwork.sif')
After completing these steps, the local subnetwork around VWF can be inspected in Cytoscape by loading the network from file
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