Solutions - Bayesian Networks in R: With Applications in Systems Biology - page 138

+ graph.edgelist(cbind(DBNlasso.50edges[, 1],
+ DBNlasso.50edges[, 2]))
> DBNlasso.nodeCoord =
+ layout.fruchterman.reingold(DBNlasso.top50)
> plot(DBNlasso.top50,
+ layout = DBNlasso.nodeCoord,
+ edge.arrow.size = 0.5, vertex.size = 10,
+ main = "Network inferred with LASSO"")

• With GeneNet :

> DBNGeneNet.top50 =
+ graph.edgelist(DBNGeneNet.50edges)
> DBNGeneNet.nodeCoord =
+ layout.fruchterman.reingold(DBNGeneNet.50edges)
> plot(DBNGeneNet.top50,
+ layout = DBNGeneNet.nodeCoord,
+ edge.arrow.size = 0.5, vertex.size = 10,
+ main = "Network inferred with GeneNet")

• With SIMoNe :

> DBNsimone.top50 =
+ graph.edgelist(cbind(DBNsimone.50edges[, 1],

+ DBNsimone.50edges[, 2]))

> DBNsimone.nodeCoord =
+ layout.fruchterman.reingold(DBNsimone.top50)
> plot(DBNsimone.top50,
+ layout = DBNsimone.nodeCoord,
+ edge.arrow.size = 0.5, vertex.size = 10,
+ main = "Network inferred with SIMoNe")

•

With G1DBN :

> DBNG1DBN.top50 =
+ graph.edgelist(cbind(DBNG1DBN.50edges[, 1],

+ DBNG1DBN.50edges[, 2]))

> DBNG1DBN.nodeCoord =
+ layout.fruchterman.reingold(DBNG1DBN.top50)
> plot(DBNG1DBN.top50, layout=DBNG1DBN.nodeCoord,
+ edge.arrow.size = 0.5, vertex.size = 10,
+ main = "Network inferred with G1DBN")
(c) Arcs common to all 4 inferred networks:
> DBNlasso.50edges.mat =
+ as.numeric(abs(DBNlasso) > lasso.thres.top50)
> DBNGeneNet.50edges.mat = matrix(0, 49, 49)