showNetwork {EBcoexpress} | R Documentation |
This function draws a network for a selected group of genes using igraph. The edges are colored in accordance with the correlation strength indicated by the inputted D matrix, ranging from red (strong negative correlation) to blue (strong positive correlation)
showNetwork(geneSet, D, condFocus, gsep = "~", layout = "kamada.kawai", seed = NULL, hidingThreshold=NULL, ...)
geneSet |
An array of genes of interest; should not be larger than a dozen or so |
D |
The correlation matrix output of makeMyD() |
condFocus |
The condition of interest for this network. Should be one of the integers in the conditions array |
gsep |
A separator that indicates a gene-pair, such as P53~MAPK1. The separator should not appear in any of the gene names |
layout |
A layout to be parsed and used by igraph. Examples include circle (the default) and kamada.kawai; see the documentation for igraph for more information. At this time it is not possible to specify parameters specific to particular layouts |
seed |
A seed to be set before invoking igraph's layout generation. This is useful for layouts such as random, where node postion is not deterministic |
hidingThreshold |
A threshold which we will shorthand by 'h'. If this value is non-NULL, all correlations in [-h, h] will not be plotted in the network. This is useful for removing clutter in busy networks will relatively high (say, 20+) numbers of genes |
... |
Other options to be passed to plot.igraph(). Networks generated by igraph require quite a bit of formatting, and it is up to the user to do so by specifying appropriate options from the following: vertex.shape=, vertex.label.cex=, vertex.color=, vertex.frame.color=, vertex.size=, vertex.label.color=, vertex.label.family=, and edge.width= The following options are hard-coded and may not be overwritten: vertex.label=geneSet, edge.arrow.mode=0, edge.color=[red/blue colors] where [red/blue colors] is determined by the correlation information contained in D, possibly overwritten in some cases if hidingThreshold is non-NULL |
Returns invisible(NULL)
John A. Dawson <jadawson@wisc.edu>
Dawson JA and Kendziorski C. An empirical Bayesian approach for identifying differential co-expression in high-throughput experiments. (2011) Biometrics. E-publication before print: http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2011.01688.x/abstract
igraph, igraph.layout
data(fiftyGenes) tinyCond <- c(rep(1,100),rep(2,25)) tinyPat <- ebPatterns(c("1,1","1,2")) D <- makeMyD(fiftyGenes, tinyCond, useBWMC=TRUE) twentyGeneNames <- dimnames(fiftyGenes)[[1]][c(1:10,26:35)] showNetwork(twentyGeneNames, D, condFocus = 1, gsep = "~", layout = "kamada.kawai", seed = 5, vertex.shape="circle", vertex.label.cex=1, vertex.color="white", edge.width=2, vertex.frame.color="black", vertex.size=20, vertex.label.color="black", vertex.label.family="sans", hidingThreshold=0.3) # showNetwork(twentyGeneNames, D, condFocus = 2, gsep = "~", layout = "kamada.kawai", seed = 5, vertex.shape="circle", vertex.label.cex=1, vertex.color="white", edge.width=2, vertex.frame.color="black", vertex.size=20, vertex.label.color="black", vertex.label.family="sans", hidingThreshold=0.3) #