View source: R/application_FBN.R
generateFBMNetwork | R Documentation |
This is the main entry of the package FBNNet that can be used to mine the gene regulatory network.
generateFBMNetwork(
timeseries_data,
method = c("kmeans", "edgeDetector", "scanStatistic"),
maxK = 4,
useParallel = FALSE,
max_deep_temporal = 1,
threshold_confidence = 1,
threshold_error = 0,
threshold_support = 1e-05,
maxFBNRules = 5,
network_only = TRUE,
verbose = FALSE
)
timeseries_data |
A list of timeseries data of samples. |
method |
Specify a method to discrete the data in the range of
('kmeans', 'edgeDetector', 'scanStatistic') for the function
|
maxK |
The maximum deep of the Orchard Cube can mine into. |
useParallel |
Optional, by default it is FALSE not to run the network inference algorithm in parallel. FALSE without parallel |
max_deep_temporal, |
a setting for Temporal Fundamental Boolean model that specifies the maximum temporal space |
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, the rest will be discarded |
network_only |
Optional for Debug purpose, if TRUE, only output the networks only, otherwise, output the Orchard cube as well. Warning, turn off this may cause memory leaking if the number of nodes is too large. |
verbose |
Optional, if it is TRUE, then output the logger information to the console. |
An object of a list contains Fundamental Boolean Network and Orchard cube (optional) if network_only set to FALSE.
Leshi Chen, leshi, chen@lincolnuni.ac.nz, chenleshi@hotmail.com
Chen et al.(2018), Front. Physiol., 25 September 2018, (Front. Physiol.)
Mussel, Hopfensitz et al. 2010, BoolNet - an R package for generation, reconstruction and analysis of Boolean networks
data('yeastTimeSeries')
network <- generateFBMNetwork(yeastTimeSeries, verbose = TRUE)
network
## draw the general graph
FBNNetwork.Graph(network)
## get the Orchard cube as well as networks
res <- generateFBMNetwork(yeastTimeSeries, network_only = FALSE)
res
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