generateNetwork {nem} | R Documentation

## Random networks and data sampling

### Description

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

### Usage

    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))

### Arguments

`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_2*Beta(α,1) + λ_3*Beta(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

### Details

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.

### Value

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

### Author(s)

Holger Froehlich, Cordula Zeller

### See Also

`getDensityMatrix`

### Examples

    	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")
      }

[Package nem version 2.56.0 Index]