KGML2igraph | R Documentation |
This function takes KGML files as input, and returns either a metabolic or a signaling network as output.
KGML2igraph(
filename,
parse.as = c("metabolic", "signaling"),
expand.complexes = FALSE,
verbose = TRUE
)
filename |
A character vector containing the KGML files to be processed. If a directory path is provided, all *.xml files in it and its subdirectories are included. |
parse.as |
Whether to process file into a metabolic or a signaling network. |
expand.complexes |
Split protein complexes into individual gene nodes. This argument is
ignored if |
verbose |
Whether to display the progress of the function. |
Users can specify whether files are processes as metabolic or signaling networks.
Metabolic networks are given as bipartite graphs, where metabolites and reactions represent
vertex types. This is constructed from <reaction> xml node in KGML file, connecting them
to their corresponding substrates and products. Each reaction vertex has genes
attribute,
listing all genes associated with the reaction. As a general rule, reactions inherit all annotation
attributes of its catalyzig genes.
Signaling network have genes as vertices and edges represent interactions, such as activiation / inhibition. Genes participating in successive reactions are also connected. Signaling parsing method processes <ECrel>, <PPrel> and <PCrel> interactions from KGML files.
To generate a genome scale network, simply provide a list of files to be parsed, or put all
file in a directory, as pass the directory path as filename
An igraph object, representing a metbolic or a signaling network.
Ahmed Mohamed
Other Database extraction methods:
SBML2igraph()
,
biopax2igraph()
if(is.loaded("readkgmlfile")){ # This is false if libxml2 wasn't available at installation.
filename <- system.file("extdata", "hsa00860.xml", package="NetPathMiner")
# Process KGML file as a metabolic network
g <- KGML2igraph(filename)
plotNetwork(g)
# Process KGML file as a signaling network
g <- KGML2igraph(filename, parse.as="signaling", expand.complexes=TRUE)
plotNetwork(g)
}
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