Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(cache = TRUE)
## ----eval=F-------------------------------------------------------------------
# library(devtools)
# install_github("ctlab/mwcsr")
## ----message=FALSE------------------------------------------------------------
library(mwcsr)
library(igraph)
## -----------------------------------------------------------------------------
data("mwcs_example")
print(mwcs_example)
summary(V(mwcs_example)$weight)
## -----------------------------------------------------------------------------
rcsolver <- rmwcs_solver()
## -----------------------------------------------------------------------------
m <- solve_mwcsp(rcsolver, mwcs_example)
print(m$graph)
print(m$weight)
## -----------------------------------------------------------------------------
get_instance_type(mwcs_example)
## -----------------------------------------------------------------------------
mwcs_example
summary(V(mwcs_example)$weight)
## -----------------------------------------------------------------------------
budget_mwcs_example <- mwcs_example
set.seed(42)
V(budget_mwcs_example)$budget_cost <- runif(vcount(budget_mwcs_example))
get_instance_type(budget_mwcs_example)
## -----------------------------------------------------------------------------
data(gmwcs_example)
gmwcs_example
summary(V(gmwcs_example)$weight)
summary(E(gmwcs_example)$weight)
## -----------------------------------------------------------------------------
data("sgmwcs_example")
sgmwcs_example
str(V(sgmwcs_example)$signal)
str(E(sgmwcs_example)$signal)
head(sgmwcs_example$signals)
## -----------------------------------------------------------------------------
data("gatom_example")
print(gatom_example)
## -----------------------------------------------------------------------------
get_instance_type(gatom_example)
## -----------------------------------------------------------------------------
gatom_instance <- normalize_sgmwcs_instance(gatom_example)
get_instance_type(gatom_instance)
## -----------------------------------------------------------------------------
gatom_instance <- normalize_sgmwcs_instance(gatom_example,
nodes.weight.column = "weight",
edges.weight.column = "weight",
nodes.group.by = "signal",
edges.group.by = "signal",
group.only.positive = TRUE)
## -----------------------------------------------------------------------------
rmwcs <- rmwcs_solver()
m <- solve_mwcsp(rmwcs, mwcs_example)
print(m$weight)
print(m$solved_to_optimality)
## -----------------------------------------------------------------------------
m <- solve_mwcsp(rmwcs, mwcs_example, max_cardinality = 10)
print(vcount(m$graph))
print(m$weight)
## -----------------------------------------------------------------------------
m <- solve_mwcsp(rmwcs, budget_mwcs_example, budget = 10)
print(sum(V(m$graph)$budget_cost))
print(m$weight)
## -----------------------------------------------------------------------------
rnc <- rnc_solver()
m <- solve_mwcsp(rnc, gmwcs_example)
print(m$weight)
print(m$solved_to_optimality)
## -----------------------------------------------------------------------------
rnc <- rnc_solver()
m <- solve_mwcsp(rnc, sgmwcs_example)
print(m$weight)
## -----------------------------------------------------------------------------
m <- NULL
for (i in 0:15) {
asolver <- annealing_solver(schedule = "boltzmann", initial_temperature = 8.0 / (2 ** i),
final_temperature = 1 / (2 ** i))
if (i != 0) {
m <- solve_mwcsp(asolver, gmwcs_example, warm_start = m)
} else {
m <- solve_mwcsp(asolver, gmwcs_example)
}
print(m$weight)
}
## -----------------------------------------------------------------------------
mst_solver <- virgo_solver(cplex_dir=NULL)
m <- solve_mwcsp(mst_solver, sgmwcs_example)
print(m$weight)
print(m$solved_to_optimality)
## ----message=FALSE,eval=FALSE-------------------------------------------------
# scip <- scipjack_solver(scipstp_bin=Sys.which("scipstp"))
# sol <- solve_mwcsp(scip, mwcs_example)
## ----message=FALSE------------------------------------------------------------
BioNetInstalled <- FALSE
if (requireNamespace("BioNet") && requireNamespace("DLBCL")) {
BioNetInstalled <- TRUE
}
## ----message=FALSE------------------------------------------------------------
if (BioNetInstalled) {
library("BioNet")
library("DLBCL")
data(dataLym)
data(interactome)
pvals <- cbind(t = dataLym$t.pval, s = dataLym$s.pval)
rownames(pvals) <- dataLym$label
pval <- aggrPvals(pvals, order = 2, plot = FALSE)
logFC <- dataLym$diff
names(logFC) <- dataLym$label
subnet <- subNetwork(dataLym$label, interactome)
subnet <- rmSelfLoops(subnet)
fb <- fitBumModel(pval, plot = FALSE)
scores <- scoreNodes(subnet, fb, fdr = 0.001)
}
## -----------------------------------------------------------------------------
if (BioNetInstalled) {
subnet
str(scores)
}
## -----------------------------------------------------------------------------
if (BioNetInstalled) {
bionet_h <- runFastHeinz(subnet, scores)
plotModule(bionet_h, scores=scores, diff.expr=logFC)
sum(scores[nodes(bionet_h)])
}
## -----------------------------------------------------------------------------
if (BioNetInstalled) {
bionet_example <- igraph.from.graphNEL(subnet, weight=FALSE) # ignoring edge weights of 1
V(bionet_example)$weight <- scores[V(bionet_example)]
get_instance_type(bionet_example)
}
## -----------------------------------------------------------------------------
if (BioNetInstalled) {
rmwcs <- rmwcs_solver()
bionet_m <- solve_mwcsp(rmwcs, bionet_example)
plotModule(bionet_m$graph, scores=scores, diff.expr=logFC)
}
## -----------------------------------------------------------------------------
if (BioNetInstalled) {
print(bionet_m$weight)
}
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