BoolNet (version 1.44)

reconstructNetwork: Reconstruct a Boolean network from time series of measurements

Description

Reconstructs a Boolean network from a set of time series or from a transition table using the best-fit extension algorithm or the REVEAL algorithm.

Usage

reconstructNetwork(measurements, 
                   method = c("bestfit", "reveal"), 
                   maxK = 5, 
                   readableFunctions = FALSE,
                   allSolutions = FALSE)

Arguments

measurements

This can either be an object of class TransitionTable as returned by latex{ }getTransitionTable, or a set of time series of measurements. In this case, measurements

method

This specifies the reconstruction algorithm to be used. If set to "bestfit", Laehdesmaeki's Best-Fit Extension algorithm is employed. This algorithm is an improvement of the algorithm by Akutsu et al. with a lower runtime complexity. It determines the fun

maxK

The maximum number of input genes for one gene to be tested. Defaults to 5.

readableFunctions

If this is true, readable DNF representations of the truth tables of the functions are generated. These DNF are displayed when the network is printed. The DNF representations are not minimized and can thus be very long. If set to FALSE, the truth table re

allSolutions

If this is true, all solutions with the minimum error and up to maxK inputs are returned. By default, allSolutions=FALSE, which means that only the solutions with both minimum error and minimum k are returned.

Value

• Returns an object of class ProbabilisticBooleanNetwork, with each alternative function of a gene having the same probability. The structure is described in detail in loadNetwork. In addition to the standard components, each alternative transition function has a component error which stores the error of the function on the input time series data. If a non-probabilistic network of class BooleanNetwork is required, a specific Boolean network can be extracted using chooseNetwork.

Details

Both algorithms iterate over all possible input combinations. While Best-Fit Extension is capable of returning functions that do not perfectly explain the measurements (for example, if there are inconsistent measurements or if maxK was specified too small), REVEAL only finds functions that explain all measurements. For more information, please refer to the cited publications.

References

H. Laehdesmaeki, I. Shmulevich and O. Yli-Harja (2003), On Learning Gene-Regulatory Networks Under the Boolean Network Model. Machine Learning 52:147--167.

T. Akutsu, S. Miyano and S. Kuhara (2000). Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics 16(8):727--734.

S. Liang, S. Fuhrman and R. Somogyi (1998), REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Pacific Symposium on Biocomputing 3:18--29.

See Also

binarizeTimeSeries, chooseNetwork

Examples

library(BoolNet)

# load example data
data(yeastTimeSeries)

# perform binarization with k-means
bin <- binarizeTimeSeries(yeastTimeSeries)

# reconstruct networks from transition table
net <- reconstructNetwork(bin$binarizedMeasurements, method="bestfit", maxK=3)

# print reconstructed net
print(net)

# plot reconstructed net
plotNetworkWiring(net)