runInverseCarnival
runInverseCarnival(
measurements,
priorKnowledgeNetwork,
weights = NULL,
carnivalOptions = defaultLpSolveCarnivalOptions()
)vector of the measurements (i.e. DoRothEA/VIPER normalised enrichment scores)
data frame of the prior knowledge network
(optional) vector of the additional weights: e.g. PROGENy pathway score or measured protein activities.
the list of options for the run. See defaultLpSolveCarnivalOptions(), defaultLpSolveCarnivalOptions, defaultCbcCarnivalOptions.
The function will return a list of results containing:
1. weightedSIF: A table with 4 columns containing the combined network solutions from CARNIVAL. It contains the Source of the interaction (Node1), Sign of the interaction (Sign), the Target of the interaction (Node2) and the weight of the interaction (Weight) which shows how often an interaction appears across all solutions.
2. nodesAttributes: A table with 6 columns containing information about infered protein activity states and attributes. It contains the Protein IDs (Node); how often this node has taken an activity of 0, 1 and -1 across the solutions (ZeroAct, UpAct, DownAct); the average activities across solutions (AvgAct); and the node attribute (measured, target, inferred).
3. sifAll: A list of separate network solutions.
4. attributesAll: A list of separate inferred node activities in each solution.
5. diagnostics: reports the convergence of optimization and reason of the termination. Only for CPLEX solver.
TODO Replace with correct description
load(file = system.file("toy_measurements_ex1.RData",
package="CARNIVAL"))
load(file = system.file("toy_network_ex1.RData",
package="CARNIVAL"))
## lpSolve
#res1 = runInverseCarnival(measurements = toy_measurements_ex1,
# priorKnowledgeNetwork = toy_network_ex1,
# carnivalOptions = defaultLpSolveCarnivalOptions())
#res1$weightedSIF ##see @return
#res1$nodesAttributes ## see @return
#res1$sifAll ## see @return
#res1$attributesAll ## see @return
## Examples for cbc and cplex are commented out because these solvers are not part of R environment
## and need to be installed separately
##
## cbc
## res2 = runInverseCarnival(measurements = toy_measurements_ex1,
## priorKnowledgeNetwork = toy_network_ex1,
## carnivalOptions = defaultCbcCarnivalOptions())
##
## res2$weightedSIF ##see @return
## res2$nodesAttributes ## see @return
## res2$sifAll ## see @return
## res2$attributesAll ## see @return
##
## cplex
## res3 = runVanillaCarnival(measurements = toy_measurements_ex1,
## priorKnowledgeNetwork = toy_network_ex1,
## carnivalOptions = defaultCplexCarnivalOptions())
##
## res3$weightedSIF ##see @return
## res3$nodesAttributes ## see @return
## res3$sifAll ## see @return
## res3$attributesAll ## see @return