library(GSNA)
# In this example, we generate a gene set network from CERNO example
# data. We begin by subsetting the CERNO data for significant results:
sig_pathways.cerno <- subset( Bai_CiHep_DN.cerno, adj.P.Val <= 0.05 )
# Now create a gene set collection containing just the gene sets
# with significant CERNO results, by subsetting Bai_gsc.tmod using
# the gene set IDs as keys:
sig_pathways.tmod <- Bai_gsc.tmod[sig_pathways.cerno$ID]
# And obtain a background gene set from differential expression data:
background_genes <- toupper( rownames( Bai_CiHep_v_Fib2.de ) )
# Build a gene set network:
sig_pathways.GSN <-
buildGeneSetNetworkJaccard(geneSetCollection = sig_pathways.tmod,
ref.background = background_genes )
# Now we can pare the network and assign subnets:
sig_pathways.GSN <- gsnPareNetGenericHierarchic( object = sig_pathways.GSN )
# Create a pared vs raw distance plot to examine the relationship between the
# raw and pared distances:
gsnParedVsRawDistancePlot( sig_pathways.GSN )
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