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.
Adjacency matrix as resulting from
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
Bender et. al. 2010: Dynamic deterministic effects propagation networks: learning signalling pathways from longitudinal protein array data; Bioinformatics, Vol. 26(18), pp. i596-i602
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.