R/api_run_carnival.R

In saezlab/CARNIVAL: A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming

#' Checks validity of all inputs of CARNIVAL
#'
#'@param perturbations (optional, if inverse CARNIVAL flavour is used further) vector of targets of perturbations.
#'@param measurements vector of the measurements (i.e. DoRothEA/VIPER normalised
#'enrichment scores)
#'@param priorKnowledgeNetwork data frame of the prior knowledge network
#'@param weights (optional) vector of the additional weights: e.g. PROGENy pathway
#'scores or measured protein activities.
#'@param carnivalOptions the list of options for the run. See defaultLpSolveCarnivalOptions(),
#'defaultCplexCarnivalOptions, defaultCbcCarnivalOptions.
#'
#' @return TRUE if everything passed the checks.
#' @export
#' @examples
#' load(file = system.file("toy_perturbations_ex1.RData",
#'                         package="CARNIVAL"))
#' load(file = system.file("toy_measurements_ex1.RData",
#'                         package="CARNIVAL"))
#' load(file = system.file("toy_network_ex1.RData",
#'                         package="CARNIVAL"))
#'
#' ## lpSolve
#' #isInputValidCarnival(perturbations = toy_perturbations_ex1,
#' #                    measurements = toy_measurements_ex1,
#' #                    priorKnowledgeNetwork = toy_network_ex1,
#' #                    carnivalOptions = defaultLpSolveCarnivalOptions())
isInputValidCarnival <- function(perturbations = NULL,
                                 measurements,
                                 priorKnowledgeNetwork,
                                 weights = NULL,
                                 carnivalOptions =
                                   defaultLpSolveCarnivalOptions()) {
  message("--- Start of the CARNIVAL pipeline ---")
  message(getTime(), " Carnival flavour: input validity check.")

  checkData( perturbations, measurements,
             priorKnowledgeNetwork, weights )

  checkCarnivalOptions(carnivalOptions)

  message(getTime(), " All inputs checks passed.")
  message(getTime(), " All tasks finished.")
  message("\n", "--- End of the CARNIVAL pipeline --- ", "\n")

  return(TRUE)
}

#'\code{generateLpFileCarnival}
#'
#'@details Prepares the input data for the run: tranforms data into lp file and .Rdata file.
#'These files can be reused to run CARNIVAL without preprocessing step using runCarnivalFromLp(..)
#'
#'@param perturbations (optional, if inverse CARNIVAL flavour is used further) vector of targets of perturbations.
#'@param measurements vector of the measurements (i.e. DoRothEA/VIPER normalised
#'enrichment scores)
#'@param priorKnowledgeNetwork data frame of the prior knowledge network
#'@param weights (optional) vector of the additional weights: e.g. PROGENy pathway
#'scores or measured protein activities.
#'@param carnivalOptions the list of options for the run. See defaultLpSolveCarnivalOptions(),
#'defaultCplexCarnivalOptions, defaultCbcCarnivalOptions.
#'
#'@return paths to .lp file and .RData file that can be used for runFromLpCarnival()
#'
#'@examples
#' load(file = system.file("toy_perturbations_ex1.RData",
#'                         package="CARNIVAL"))
#' load(file = system.file("toy_measurements_ex1.RData",
#'                         package="CARNIVAL"))
#' load(file = system.file("toy_network_ex1.RData",
#'                         package="CARNIVAL"))
#'
#' ## lpSolve
#' #res1 = generateLpFileCarnival(perturbations = toy_perturbations_ex1,
#' #                             measurements = toy_measurements_ex1,
#' #                             priorKnowledgeNetwork = toy_network_ex1,
#' #                             carnivalOptions = defaultLpSolveCarnivalOptions())
#'
#' #res1["lpFile"] ##path to generated lp file
#' #res1["parsedDataFile"] ##path to data file used during generation
#'
#' ## 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 = generateLpFileCarnival(perturbations = toy_perturbations_ex1,
#' ##                               measurements = toy_measurements_ex1,
#' ##                               priorKnowledgeNetwork = toy_network_ex1,
#' ##                               carnivalOptions = defaultCbcCarnivalOptions())
#' ##
#' ## res2["lpFile"] ##path to generated lp file
#' ## res2["parsedDataFile"] ##path to data file used during generation
#' ##
#' ## cplex
#' ## res3 = generateLpFileCarnival(perturbations = toy_perturbations_ex1,
#' ##                              measurements = toy_measurements_ex1,
#' ##                              priorKnowledgeNetwork = toy_network_ex1,
#' ##                              carnivalOptions = defaultCplexCarnivalOptions())
#' ##
#' ## res3["lpFile"] ##path to generated lp file
#' ## res3["parsedDataFile"] ##path to data file used during generation
#'@export
generateLpFileCarnival <- function( perturbations = NULL,
                                    measurements,
                                    priorKnowledgeNetwork,
                                    weights = NULL,
                                    carnivalOptions =
                                      defaultLpSolveCarnivalOptions()) {
  message("--- Start of the CARNIVAL pipeline ---")
  message(getTime(), " Carnival flavour: LP generator")

  dataPreprocessed <- checkData( perturbations, measurements,
                                 priorKnowledgeNetwork, weights )

  checkCarnivalOptions(carnivalOptions)
  carnivalOptions <- collectMetaInfo(carnivalOptions)

  prepareForCarnivalRun(dataPreprocessed, carnivalOptions)
  cleanupCarnival(carnivalOptions)

  message(getTime(), " All tasks finished.")
  message("\n", "--- End of the CARNIVAL pipeline --- ", "\n")

  return(c("lpFile" = carnivalOptions$filenames$lpFilename,
           "parsedDataFile" = carnivalOptions$filenames$parsedData))
}

#'\code{runVanillaCarnival}
#'
#'@details Runs full CARNIVAL pipeline, vanilla(classic) flavour.
#'
#'@param perturbations vector of targets of perturbations.
#'@param measurements vector of the measurements (i.e. DoRothEA/VIPER normalised
#'enrichment scores)
#'@param priorKnowledgeNetwork data frame of the prior knowledge network
#'@param weights (optional) vector of the additional weights: e.g. PROGENy pathway
#'score or measured protein activities.
#'@param carnivalOptions the list of options for the run. See defaultLpSolveCarnivalOptions(),
#'defaultLpSolveCarnivalOptions, defaultCbcCarnivalOptions.
#'
#'@return 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.
#'
#'@examples
#'load(file = system.file("toy_perturbations_ex1.RData",
#'                        package="CARNIVAL"))
#'load(file = system.file("toy_measurements_ex1.RData",
#'                        package="CARNIVAL"))
#'load(file = system.file("toy_network_ex1.RData",
#'                        package="CARNIVAL"))
#'
#'## lpSolve
#'#res1 = runVanillaCarnival(perturbations = toy_perturbations_ex1,
#'#                          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 = runVanillaCarnival(perturbations = toy_perturbations_ex1,
#'##                               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(perturbations = toy_perturbations_ex1,
#'##                              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
#'
#'@author Enio Gjerga, Olga Ivanova 2020-2021 \email{carnival.developers@gmail.com}
#'
#'@export
runVanillaCarnival <- function( perturbations,
                                measurements,
                                priorKnowledgeNetwork,
                                weights = NULL,
                                carnivalOptions =
                                  defaultLpSolveCarnivalOptions()) {

  message("--- Start of the CARNIVAL pipeline ---")
  message(getTime(), " Carnival flavour: vanilla")

  dataPreprocessed <- checkData( perturbations, measurements,
                                 priorKnowledgeNetwork, weights )

  checkCarnivalOptions(carnivalOptions)
  carnivalOptions <- collectMetaInfo(carnivalOptions)

  result <- solveCarnival(dataPreprocessed, carnivalOptions)
  cleanupCarnival(carnivalOptions)

  message(getTime(), " All tasks finished.")
  message("\n", "--- End of the CARNIVAL pipeline --- ", "\n")

  return(result)
}

#'\code{runCarnivalFromLp}
#'
#'@details Runs CARNIVAL pipeline with preparsed data - lp file and Rdata file containing variables for ILP formulation.
#'
#'@param lpFile full path to .lp file
#'@param parsedDataFile full path to preprocessed .RData file
#'@param carnivalOptions the list of options for the run. See defaultLpSolveCarnivalOptions(),
#'defaultLpSolveCarnivalOptions, defaultCbcCarnivalOptions.
#'
#'@return 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.
#'
#'@examples
#'lpFilePath = system.file("toy_lp_file_ex1.lp",
#'                          package="CARNIVAL")
#'
#'parsedDataFilePath = system.file("toy_parsed_data_ex1.RData",
#'                                 package="CARNIVAL")
#'
#'## lpSolve
#'#res1 = runFromLpCarnival(lpFile = lpFilePath,
#'#                         parsedDataFile = parsedDataFilePath,
#'#                         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 = runFromLpCarnival(lpFile = lpFilePath,
#'##                          parsedDataFile = parsedDataFilePath,
#'##                          carnivalOptions = defaultLpCbcCarnivalOptions())
#'##
#'## res2$weightedSIF ##see @return
#'## res2$nodesAttributes ## see @return
#'## res2$sifAll ## see @return
#'## res2$attributesAll ## see @return
#'##
#'## cplex
#'## res3 = runFromLpCarnival(lpFile = lpFilePath,
#'##                          parsedDataFile = parsedDataFilePath,
#'##                          carnivalOptions = defaultLpCplexCarnivalOptions())
#'##
#'## res3$weightedSIF ##see @return
#'## res3$nodesAttributes ## see @return
#'## res3$sifAll ## see @return
#'## res3$attributesAll ## see @return
#'
#'@author Enio Gjerga, Olga Ivanova 2020-2021 \email{carnival.developers@gmail.com}
#'
#'@export
runFromLpCarnival <- function(lpFile = "",
                              parsedDataFile = "",
                              carnivalOptions =
                                defaultLpSolveCarnivalOptions()) {

  message("--- Start of the CARNIVAL pipeline ---")
  message(getTime(), " Carnival flavour: LP run")
  checkCarnivalOptions(carnivalOptions)
  carnivalOptions <- collectMetaInfo(carnivalOptions)

  result <- solveCarnivalFromLp( lpFile = lpFile,
                                 parsedDataFile = parsedDataFile,
                                 carnivalOptions = carnivalOptions )
  cleanupCarnival(carnivalOptions)

  message(getTime(), " All tasks finished.")
  message("\n", "--- End of the CARNIVAL pipeline --- ", "\n")

  return(result)
}

#'\code{runInverseCarnival}
#'
#'@details TODO Replace with correct description
#'
#'@param measurements vector of the measurements (i.e. DoRothEA/VIPER normalised
#'enrichment scores)
#'@param priorKnowledgeNetwork data frame of the prior knowledge network
#'@param weights (optional) vector of the additional weights: e.g. PROGENy pathway
#'score or measured protein activities.
#'@param carnivalOptions the list of options for the run. See defaultLpSolveCarnivalOptions(),
#'defaultLpSolveCarnivalOptions, defaultCbcCarnivalOptions.
#'
#'@return 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.
#'
#'@examples
#'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
#'
#'@author Enio Gjerga, Olga Ivanova 2020-2021 \email{carnival.developers@gmail.com}
#'
#'@export
runInverseCarnival <- function( measurements,
                                priorKnowledgeNetwork,
                                weights = NULL,
                                carnivalOptions =
                                  defaultLpSolveCarnivalOptions() ){
  message(" ")
  message("--- Start of the CARNIVAL pipeline ---")
  message(getTime(), " Carnival flavour: inverse")

  dataPreprocessed <- checkData(  perturbations = NULL,
                                  measurements,
                                  priorKnowledgeNetwork, weights )

  checkCarnivalOptions(carnivalOptions)
  carnivalOptions <- collectMetaInfo(carnivalOptions)

  result <- solveCarnival( dataPreprocessed, carnivalOptions )
  cleanupCarnival(carnivalOptions)

  message(getTime(), " All tasks finished.")
  message("\n", "--- End of the CARNIVAL pipeline --- ", "\n")

  return(result)
}

runCarnivalWithManualConstraints <- function(perturbations,
                                             measurements,
                                             priorKnowledgeNetwork,
                                             pathwayWeights = NULL,
                                             constraints = c(),
                                             carnivalOptions =
                                               defaultLpSolveCarnivalOptions()) {
  stop("Function is not implemented yet.")
  return(NULL)
}

#TODO examples! update the list of params - DF vs vectors
#'\code{runCARNIVAL}
#'
#'@details Run CARNIVAL pipeline using to the user-provided list of inputs or
#'run CARNIVAL built-in examples. The function is from v1.2 of CARNIVAL
#' and is left for backward compatibility.
#'
#'@param inputObj Data frame of the list for target of perturbation - optional
#'or default set to NULL to run invCARNIVAL when inputs are not known.
#'@param measObj Data frame of the measurement file (i.e. DoRothEA normalised
#'enrichment scores) - always required.
#'@param netObj Data frame of the prior knowledge network - always required.
#'@param weightObj Data frame of the additional weight (i.e. PROGENy pathway
#'score or measured protein activities) - optional or default set as NULL to run
#'CARNIVAL without weights.
#'@param solverPath Path to executable cbc/cplex file - default set to NULL, in
#'which case the solver from lpSolve package is used.
#'@param solver Solver to use: lpSolve/cplex/cbc (Default set to lpSolve).
#'@param timelimit CPLEX/Cbc parameter: Time limit of CPLEX optimisation in
#'seconds (default set to 3600).
#'@param mipGAP CPLEX parameter: the absolute tolerance on the gap between the
#'best integer objective and the objective of the best node remaining. When this
#'difference falls below the value of this parameter, the linear integer
#'optimization is stopped (default set to 0.05)
#'@param poolrelGAP CPLEX/Cbc parameter: Allowed relative gap of accepted
#'solution comparing within the pool of accepted solution (default: 0.0001)
#'@param limitPop CPLEX parameter: Allowed number of solutions to be generated
#'(default: 500)
#'@param poolReplace CPLEX parameter: Replacement strategy of solutions in the
#'pool (0,1,2 - default: 2 = most diversified solutions)
#'@param poolCap CPLEX parameter: Allowed number of solution to be kept in the
#'pool of solution (default: 100)
#'@param poolIntensity CPLEX parameter: Intensity of solution searching
#'(0,1,2,3,4 - default: 4)
#'@param alphaWeight Objective function: weight for mismatch penalty (default:
#'1 - will only be applied once measurement file only contains discrete values)
#'@param betaWeight Objective function: weight for node penalty (default: 0.2)
#'@param threads CPLEX/CBC parameter: Number of threads to use
#'default: 0 for maximum number possible threads on system
#'@param dir_name Specify directory name to store results. by default set to
#'NULL
#'@param cleanTmpFiles logic (default-TRUE), specifying if the tmp files made by
#'solvers should be cleaned after run.
#'@param keepLPFiles logic (default=TRUE), specifying if the LP file should be
#'kept.
#'@param clonelog determines if CPLEX clones the log files in case of 
#'multi-threaded optimization, default: -1, (no cloning)

#'@return 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.
#'
#'@author Enio Gjerga, 2020 \email{carnival.developers@gmail.com}
#'
#'@examples
#'load(file = system.file("toy_perturbations_ex1.RData",
#'                        package="CARNIVAL"))
#'load(file = system.file("toy_measurements_ex1.RData",
#'                        package="CARNIVAL"))
#'load(file = system.file("toy_network_ex1.RData",
#'                        package="CARNIVAL"))
#'
#'## lpSolve
#'#res1 = runCARNIVAL(inputObj = toy_perturbations_ex1,
#'#                    measObj = toy_measurements_ex1,
#'#                    netObj = toy_network_ex1,
#'#                    solver = 'lpSolve')
#'
#'#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 = runCARNIVAL(inputObj = toy_perturbations_ex1,
#'##                    measObj = toy_measurements_ex1,
#'##                    netObj = toy_network_ex1,
#'##                    solver = 'cbc')
#'##
#'## res2$weightedSIF ##see @return
#'## res2$nodesAttributes ## see @return
#'## res2$sifAll ## see @return
#'## res2$attributesAll ## see @return
#'##
#'## cplex
#'## res3 = runCARNIVAL(inputObj = toy_perturbations_ex1,
#'##                    measObj = toy_measurements_ex1,
#'##                    netObj = toy_network_ex1,
#'##                    solver = 'cplex')
#'##
#'## res3$weightedSIF ##see @return
#'## res3$nodesAttributes ## see @return
#'## res3$sifAll ## see @return
#'## res3$attributesAll ## see @return
#'
#'@import readr
#'@import lpSolve
#'@import igraph
#'@import rmarkdown
#'
#'@export
runCARNIVAL <- function(inputObj = NULL,
                        measObj = measObj,
                        netObj = netObj,
                        weightObj  = NULL,
                        solverPath = NULL,
                        solver = c('lpSolve', 'cplex', 'cbc', 'gurobi'),
                        timelimit = 3600,
                        mipGAP = 0.05,
                        poolrelGAP = 0.0001,
                        limitPop = 500,
                        poolCap = 100,
                        poolIntensity = 4,
                        poolReplace = 2,
                        alphaWeight = 1,
                        betaWeight = 0.2,
                        threads = 0,
                        cleanTmpFiles = TRUE,
                        keepLPFiles = TRUE,
                        clonelog = -1,
                        dir_name = getwd()) {
  solver <- match.arg(solver)

  opts <- list(solverPath = solverPath,
               solver = solver,
               timelimit = timelimit,
               mipGap = mipGAP,
               poolrelGap = poolrelGAP,
               limitPop = limitPop,
               poolCap = poolCap,
               poolIntensity = poolIntensity,
               poolReplace = poolReplace,
               alphaWeight = alphaWeight,
               betaWeight = betaWeight,
               threads = threads,
               cleanTmpFiles = cleanTmpFiles,
               keepLPFiles = keepLPFiles,
               clonelog = clonelog,
               workdir = dir_name,
               outputFolder = dir_name)

  if (is.data.frame(inputObj)) {
    perturbationNames <- colnames(inputObj)
    inputObj <- as.vector(t(inputObj))
    names(inputObj) <- perturbationNames
  }

  if (is.data.frame(measObj)) {
    measurementsNames <- colnames(measObj)
    measObj <- as.vector(t(measObj))
    names(measObj) <- measurementsNames
  }
  
  if (is.data.frame(weightObj)) {
      weightsNames <- colnames(weightObj)
      weightObj <- as.vector(t(weightObj))
      names(weightObj) <- weightsNames
  }

  if (is.null(inputObj)){
    result <- runInverseCarnival(measurements = measObj,
                                 priorKnowledgeNetwork = netObj,
                                 weights = weightObj,
                                 carnivalOptions = opts)
  } else {
    result <- runVanillaCarnival(perturbations = inputObj,
                                 measurements = measObj,
                                 priorKnowledgeNetwork = netObj,
                                 weights = weightObj,
                                 carnivalOptions = opts)
  }

  return(result)

}