RevNet.JacobianMethod: Reverse engineering of networks: Penalized Jacobian method

In MetStaT: Statistical metabolomics tools

Description

Network connections are estimated by calculating the Jacobian Matrix of the network. Details of algorithm and the theory behind the algorithm can be found in the references section. This method needs high frequency sampling data of small perturbations and steady state concentrations.

Usage

  RevNet.JacobianMethod(data, delta.t, steady.state.concentrations, lamba.penalty.par, 
kappa.penalty.par, jacobian.threshold)

Arguments

data	multi-dimensional sampling matrix: time x variables x experiments
delta.t	time between subsequent time points used to calculate the fourth order approximation to the Jacobian. It the data are not evenly sampled interpolation can be used to obtain evenly distributed data with time interval delta.t.
steady.state.concentrations	a vector indicating the steady concentrations of the variables (must match second dimension size of 'data')
lamba.penalty.par	lambda penalty parameter to ensure sparsity in the Jabocian matrix
kappa.penalty.par	kappa penalty parameter to ensure sparsity in the Jabocian matrix
jacobian.threshold	threshold above which values in the found Jacobian matrix indicate an edge in the network.

Value

A connectivity matrix is returned.

Author(s)

Diana Hendrickx and Tim Dorscheidt

References

Reverse engineering of metabolic networks, a critical assessment. Diana M. Hendrickx, Margriet M. W. B. Hendriks, Paul H. C. Eilers, Age K. Smilde and Huub C. J. Hoefsloot. Mol. BioSyst, Volume 7:2 (2011) pages 511-520

Examples

data(RevNetJacobian)
RevNet.JacobianMethod(RevNetJacobian, 0.01, SteadyState, 0.0002, 1, 0.0001)

MetStaT documentation built on May 2, 2019, 1:45 p.m.