generateNetwork: Random networks and data sampling
In cbg-ethz/pcNEM: Probabilistic combinatorial nested effects model

Description Usage Arguments Details Value Author(s) See Also Examples

1. Random network generation; 2. sampling of data from a given network topology

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sampleRndNetwork(Sgenes, scaleFree=TRUE, gamma=2.5, maxOutDegree=length(Sgenes), maxInDegree=length(Sgenes), trans.close=TRUE, DAG=FALSE)

sampleData(Phi, m, prob=NULL, uninformative=0, type="binary", replicates=4, typeI.err=0.05, typeII.err=0.2, alpha=sample(seq(0.1,0.9,by=0.1),ncol(Phi),replace=TRUE), beta=sample(5:50,ncol(Phi),replace=TRUE), lambda=matrix(sample(seq(0.01,0.49,by=0.01),ncol(Phi)*2,replace=TRUE),ncol=2), meansH1=rep(0.5, ncol(Phi)), meansH0=rep(-0.5, ncol(Phi)), sdsH1=sample(seq(0.1,1,by=0.1),ncol(Phi),replace=TRUE), sdsH0=sample(seq(0.1,1,by=0.1),ncol(Phi),replace=TRUE))

`Sgenes`	character vector of S-genes
`scaleFree`	should the network topology be scale free?
`gamma`	for scale free networks: out-degrees of nodes are sampled from \frac{1}{Z} (0:maxOutDegree)^{-γ}*, where Z is a normalization factor
`maxOutDegree`	maximal out-degree of nodes
`maxInDegree`	maximal in-degree of nodes prior to transitive closure
`trans.close`	Should the transitive closure of the graph be returned? Default: TRUE
`DAG`	Should only DAGs be sampled? Default: FALSE
`Phi`	adjacency matrix
`m`	number of E-genes to sample
`prob`	probability for each S-gene to get an E-gene attached
`uninformative`	additional number of uninformative E-genes, i.e. E-genes carrying no information about the nested structure
`type`	"binary" = binary data; "density" = log 'p-value' densities sampled from beta-uniform mixture model; "lodds" = log odds sampled from two normal distributions
`replicates`	number of replicate measurements to simulate for binary data
`typeI.err`	simulated type I error for binary data
`typeII.err`	simulated type II error for binary data
`alpha`	parameter for Beta(α,1) distribution: one parameter per S-gene
`beta`	parameter for Beta(1,β) distribution: one parameter per S-gene
`lambda`	mixing coefficients for beta-uniform mixture model of the form: λ_1 + λ_2Beta(α,1) + λ_3Beta(1,β). There is a vector of 3 mixing coefficients per model and one model per S-gene.
`meansH1`	normal distribution means of log odds ratios under the hypothesis of expecting an effect: one mean per S-gene
`meansH0`	normal distribution means of log odds ratios under the null hypothesis: one mean per S-gene
`sdsH1`	normal distribution standard deviations of log odds values under the hypothesis of expecting an effect: one sd per S-gene
`sdsH0`	normal distribution standard deviations of log odds values under the null hypothesis: one sd per S-gene

Random networks are generated as follows: For each S-gene S_{k} we randomly choose the number o of outgoing edges between 0 and maxOutDegree. This is either done uniform randomly or, if scale free networks are created, according to a power law distribution specified by gamma. We then select o S-genes having at most maxInDegree ingoing edge and connected S_{k} to them.

The function sampleData samples data from a given network topology as follows: We first attach E-genes to S-genes according to the probabilities prob (default: uniform). We then simulate knock-downs of the individual S-genes. For those E-genes, where no effects are expected, values are sampled from a null distribution, otherwise from an alternative distribution. In the simplest case we only sample binary data, where 1 indicates an effect an 0 no effect. Alternatively, we can sample log "p-value" densities according to a beta-uniform mixture model, where the null distribution is uniform and the alternative a mixture of two beta distributions. A third possibility is to sample log odds ratios, where alternative and null distribution are both normal.

For sampleRndNetwork an adjacency matrix, for sampleData a data matrix, for sampleData.BN a data matrix and a linking of effects to signals.

Holger Froehlich, Cordula Zeller

getDensityMatrix

	Phi = sampleRndNetwork(paste("S",1:5,sep=""))
	D = sampleData(Phi, 100, type="density")$D	
  if(require(Rgraphviz)){
	  plot(as(transitive.reduction(Phi),"graphNEL"), main="original graph")
	  x11()
	  plot.nem(nem(D, control=set.default.parameters(unique(colnames(D)), type="CONTmLLBayes")), transitiveReduction=TRUE, SCC=FALSE, main    ="inferred graph")
  }