#VisuNet installation
devtools::install_github('komorowskilab/VisuNet')
#load package
library(VisuNet)
#sample autism-control decision table
View(head(autcon))
#creating the rule-based classifier using ROSETTA from the R.ROSETTA package
autcon_rules <- rosetta(autcon)
#rule-based model quality
View(autcon_rules$quality)
#input rule data
rules <- autcon_rules$main
#required data variables
rules = rules[,c("FEATURES","DISC_CLASSES","DECISION",
"SUPP_RHS","ACC_RHS","PVAL")]
View(head(rules))
#Run VisuNet
vis_out <- visunet(rules)
#input types: R.ROSETTA data.frame ("RDF"),
#ROSETTA GUI output ("RGUI") or Line by line format ("L").
#Node color type: discretization levels ("DL")
#or accuracy value ("A")
View(head(vis_out$all$nodes))
#Nodes customisation
#mark autism-related genes as stars
#genes reported in databases of autism associations
aut_genes <- c('TSPOAP1', 'COX2','NCS1','RHPN1','FLRT2',
'BAHD1','NCKAP5L','PPOX', 'NGR2',
'ATXN8OS','DEPDC1')
#create the new vector of variables: shape
nodes_RNO <- vis_out$all$nodes
nodes_RNO$shape <- rep('dot', length(nodes_RNO$label))
nodes_RNO$shape[which(as.character(nodes_RNO$label) %in% aut_genes)] <- 'star'
nodes_RNO$shape
#create the node object list
nodesL <- list(nodes = nodes_RNO,CustCol = c('shape'))
#rerun VisuNet with a new shape of nodes
vis_out2 <- visunet(rules, CustObjectNodes = nodesL)
#Edges customisation
#mark the interaction between COX2 and MAP7 genes
edges_RNO <- vis_out2$all$edges
edges_RNO$label2
edges_RNO$arrows <- rep('enabled', length(edges_RNO$label2))
edges_RNO$arrows[which(edges_RNO$label2 == 'COX2=3-MAP7=2')] <- 'to'
#create the edge object list
edgesL <- list(edges = edges_RNO,CustCol = c('arrows'))
#rerun VisuNet with a new variable
vis_out3 <- visunet(rules, CustObjectNodes = nodesL, CustObjectEdges = edgesL)
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