nem.calcSignificance {nem} | R Documentation |
Assess statistical significance of a network hypothesis compared to a random, an empty and a fully connected network.
nem.calcSignificance(D, Phi, likelihood, N=1000, seed=1, type="mLL", Pe=NULL, para=NULL, hyperpara=NULL, delta=1, selEGenes=FALSE)
D |
data matrix with experiments in the columns (binary or continious) |
Phi |
network hypothesis (adjacency matrix) |
likelihood |
the network's marginal log-likelihood |
N |
number of random networks to sample |
seed |
random seed |
type |
mLL or FULLmLL or CONTmLL or CONTmLLDens or CONTmLLRatio |
Pe |
prior of effect reporter positions in the phenotypic hierarchy (same dimension as D) |
para |
vector of length two: false positive rate and false negative rate for non-binary data. Used by mLL |
hyperpara |
vector of length four: used by FULLmLL() for binary data |
delta |
regularization parameter for automated E-gene subset selection (CONTmLLRatio only) |
selEGenes |
automated E-gene subset selection (includes tuning of delta for CONTmLLRatio) |
Given data, N random network hypotheses from a null distribution are drawn as follows: For each S-gene $S_k$ we randomly choose a number o of outgoing edges between 0 and 3. We then select o S-genes having at most 1 ingoing edge, connected $S_k$ to them and transitively closed the graph. For all random network hypotheses it is counted, how often their likelihood is bigger than that of the given network. This yields an exact p-value.
Comparison of the likelihood of the given network to an empty and a fully connected gives us Bayes factors, which are also returned by the method.
p.value |
p-value of the network according to the null hypothesis that it is random |
BFzero |
marginal log-likelihood difference (weight of evidence) to the empty network |
BFcomplete |
marginal log-likelihood difference (weight of evidence) to the fully connected network |
Holger Froehlich
## Not run: data("BoutrosRNAi2002") D <- BoutrosRNAiDiscrete[,9:16] p <- c(.13,.05) res = nem(D, para=p) # get best network nem.calcSignificance(D,as(res$graph,"matrix"),res$mLL, para=p) # assess its statistical significance ## End(Not run)