| createCytoFiles {MetaNetwork} | R Documentation |
Create visualization files for Cytoscape, the network visualization software.
Any correlation higher than simThres is written into Cytoscape network
files and can be loaded into Cytoscape for visualization.
createCytoFiles(corrMatrix, filename, simThres = NULL, hideNodes = T)
corrMatrix |
matrix for pair-wise correlation. See corrSecondOrder example data. |
filename |
network file name, without extension. Extensions .sif and .eda will be added for network and edge attribute files, respectively. |
simThres |
(optional) numeric similarity threshold if the values in matrix are similarity values such as correlation coefficients. Default is NULL. |
hideNodes |
(optional) logical value to hide nodes without any significant links. Default is TRUE |
A network file (filename.sif) and edge attribute file
(filename.eda) are generated.
Jingyuan Fu <j.fu@rug.nl>, Morris Swertz <m.a.swertz@rug.nl>, Ritsert Jansen <r.c.jansen@rug.nl>
Fu J, Swertz MA, Keurentjes JJB, Jansen RC. MetaNetwork: a computational tool for the genetic study of metabolism. Nature Protocols (2007).
http://gbic.biol.rug.nl/supplementary/2007/MetaNetwork
Use cor, qtlCorrZeroOrder and
qtlCorrSecondOrder to calculate corrMatrix correlation matrix.
Use MetaNetwork for automated appliction of this function as
part a genetic analysis protocol on metabolites.
##NOTE: this method can be used on any correlation matrix.
#Here we use MetaNetwork methods.
## load the example data provided with this package
data(markers)
data(genotypes)
data(traits)
##OR: load your own data
#markers <- loadData("markers.csv")
#genotypes <- loadData("genotypes.csv")
#traits <- loadData("traits.csv")
##calculate the two part qtl
qtlProfiles <- qtlMapTwoPart(genotypes=genotypes, traits=traits, spike=4)
##set the qtl threshold
qtlThres <- 3.79
##OR: estimate the threshold yourself
#qtlThres <- qtlThreshold(genotypes, traits, spike=4)
##calculate zero order correlation
corrZeroOrder <- qtlCorrZeroOrder(markers, qtlProfiles, qtlThres)
##calculate second order correlation
corrSecondOrder <- qtlCorrSecondOrder( corrZeroOrder )
##set the correlation threshold
corrThres <- 0.14
##OR: estimate qtlCorrThreshold yourself
#corrThres <- qtlCorThreshold(markers, genotypes, traits, spike=4, qtlThres=qtlThres)
##create cytoscape files "mynetwork.sif" and "mynetwork.eda"
createCytoFiles(corrSecondOrder, "mynetwork", simThres = corrThres)
cat("cytofiles mynetwork.sif and mynetwork.eda created\n")