adjacencyMatrix_to_logicalRules: Convert adjacency matrix to logical rules.
In ddepn: Dynamic Deterministic Effects Propagation Networks: Infer signalling networks for timecourse RPPA data.

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Convert adjacency matrices of ddepn consensus nets to logical activation/inhibition rules of network component, i.e. target, wiring for further analysis with the BoolNet-package, e.g. for perturbation simulations, according to its loadNetwork function format.

1	adjacencyMatrix_to_logicalRules(adjMatrix, outfile)

`adjMatrix`	Adjacency matrix as resulting from `create_signetwork` for example.
`outfile`	Path to txt file where to store the output.

The input adjacency matrix must have the network components, e.g. the protein names, as row names which must be identical to the column names. As typical for the consensus net obtained from ddepn network reconstructions, the following number coding holds for the matrix:

1: target in row activates target in column,
2: target in row inhibits target in column,
0: no relation of net components.

A text file containing row-wise activation rules per network component.

The format of the output file is especially adapted to the loadNetwork function format of the BoolNet-package.

Silvia von der Heyde

DDEPN
Bender et. al. 2010: Dynamic deterministic effects propagation networks: learning signalling pathways from longitudinal protein array data; Bioinformatics, Vol. 26(18), pp. i596-i602

BoolNet
S. A. Kauffman (1969), Metabolic stability and epigenesis in randomly constructed nets. J. Theor. Biol. 22:437–467.

S. A. Kauffman (1993), The Origins of Order. Oxford University Press.

create_signetwork, loadNetwork

## Not run: 
library(ddepn)
library(BoolNet)

# create example adjacency matrix
example.mat <- matrix(sample(0:2, size=16, replace=TRUE), nrow=4,
 ncol=4, dimnames=list(x=paste("protein",letters[1:4],sep="_"),
  y=paste("protein",letters[1:4],sep="_")))

# define output file
example.out <- "exampleOutput.txt"

# convert adjacency matrix to logical rules
adjacencyMatrix_to_logicalRules(adjMatrix=example.mat,
 outfile=example.out)

## End(Not run)