generateNetwork             package:nem             R Documentation

_R_a_n_d_o_m _n_e_t_w_o_r_k_s _a_n_d _d_a_t_a _s_a_m_p_l_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     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))

_A_r_g_u_m_e_n_t_s:

  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)^{-gamma}, 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(alpha,1) distribution: one parameter per
          S-gene

    beta: parameter for Beta(1,beta) distribution: one parameter per
          S-gene

  lambda: mixing coefficients for beta-uniform mixture model of the
          form: lambda_1 + lambda_2*Beta(alpha,1) +
          lambda_3*Beta(1,beta). 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

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

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

_A_u_t_h_o_r(_s):

     Holger Froehlich <URL: http:/www.dkfz.de/mga2/people/froehlich>,
     Cordula Zeller

_S_e_e _A_l_s_o:

     'getDensityMatrix'

_E_x_a_m_p_l_e_s:

             Phi = sampleRndNetwork(paste("S",1:5,sep=""))
             D = sampleData(Phi, 100, type="density")$D      
             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")

