Mine FBN Networks from an Orchard cube

Mine FBN Networks from an Orchard cube

mineFBNNetwork(
  fbnGeneCube,
  genes = NULL,
  useParallel = FALSE,
  threshold_confidence = 1,
  threshold_error = 0,
  threshold_support = 1e-05,
  maxFBNRules = 5
)

Arguments

fbnGeneCube

A pre constructed Orchard cube

genes

The target genes in the output

useParallel

An option turns on parallel

threshold_confidence

A threshold of confidence (between 0 and 1) that used to filter the Fundamental Boolean functions

threshold_error

A threshold of error rate (between 0 and 1) that used to filter the Fundamental Boolean functions

threshold_support

A threshold of support (between 0 and 1) that used to filter the Fundamental Boolean functions

maxFBNRules

The maximum rules per type (Activation and Inhibition) per gene can be mined or filtered, the rest will be discarded

Value

A object of FBN network

References

Chen et al.(2018), Front. Physiol., 25 September 2018, (Front. Physiol.)

Author

Leshi Chen, leshi, chen@lincolnuni.ac.nz

Examples

require(BoolNet)
data('ExampleNetwork')
initialStates <- generateAllCombinationBinary(ExampleNetwork$genes)
trainingseries <- genereateBoolNetTimeseries(ExampleNetwork,
                                           initialStates,
                                           43,
                                           type='synchronous')
cube<-constructFBNCube(target_genes = ExampleNetwork$genes,
                       conditional_genes = ExampleNetwork$genes,
                       timeseriesCube = trainingseries,
                       maxK = 4,
                       temporal = 1,
                       useParallel = FALSE)
NETWORK <- mineFBNNetwork(cube,ExampleNetwork$genes)
NETWORK
#> Fundamental Boolean Network with  5 genes
#> Genes involved:
#> Gene1, Gene2, Gene3, Gene4, Gene5
#> 
#> Networks:
#> Multiple Transition Functions for Gene1 with decay value = 1:
#> Gene1_1_Activator: Gene1 = Gene1 (Confidence: 1, TimeStep: 1)
#> Gene1_1_Inhibitor: Gene1 = !Gene1 (Confidence: 1, TimeStep: 1)
#> 
#> Multiple Transition Functions for Gene2 with decay value = 1:
#> Gene2_1_Activator: Gene2 = Gene1&!Gene4&Gene5 (Confidence: 1, TimeStep: 1)
#> Gene2_1_Inhibitor: Gene2 = !Gene1 (Confidence: 1, TimeStep: 1)
#> Gene2_2_Inhibitor: Gene2 = Gene4 (Confidence: 1, TimeStep: 1)
#> Gene2_3_Inhibitor: Gene2 = !Gene5 (Confidence: 1, TimeStep: 1)
#> 
#> Multiple Transition Functions for Gene3 with decay value = 1:
#> Gene3_1_Activator: Gene3 = Gene3 (Confidence: 1, TimeStep: 1)
#> Gene3_1_Inhibitor: Gene3 = !Gene3 (Confidence: 1, TimeStep: 1)
#> 
#> Multiple Transition Functions for Gene4 with decay value = 1:
#> Gene4_1_Activator: Gene4 = !Gene1&Gene3 (Confidence: 1, TimeStep: 1)
#> Gene4_2_Activator: Gene4 = Gene3&!Gene5 (Confidence: 1, TimeStep: 1)
#> Gene4_1_Inhibitor: Gene4 = !Gene3 (Confidence: 1, TimeStep: 1)
#> Gene4_2_Inhibitor: Gene4 = Gene1&Gene5 (Confidence: 1, TimeStep: 1)
#> 
#> Multiple Transition Functions for Gene5 with decay value = 1:
#> Gene5_1_Activator: Gene5 = !Gene2 (Confidence: 1, TimeStep: 1)
#> Gene5_1_Inhibitor: Gene5 = Gene2 (Confidence: 1, TimeStep: 1)