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")