Contingency table values for the intersection of a probabilistic network of regulator-target gene edges with a reference network with incomplete knowledge.
A network represented as a data frame in which each row corresponds to a directed edge (regulator-gene pair) with nonzero estimated probability. The first column gives the name of the regulator, the second column gives the name of the regulated gene, and the third column gives the estimated probability for the regulator-gene pair. If the third column is omitted it is assumed that all edges are assigned probability 1.
A reference network represented as a two-column data frame in which each row corresponds to a regulator-gene pair (or network edge), and the columns give the corresponding regulator and target gene names, respectively. This reference network is used as the standard for determining contingency table entries.
A 2-column matrix of regulatory relationships that were known (hard-coded)
in the modeling process that produced
Threshold values on the probability of edges being in the network for which contingency tables are desired. The default is all distinct nonzero probabilities in the network. Nonzero thresholds are treated as inclusive. Thresholds should be specified as probabilities rather than percentages.
For real gene networks, complete information on the true underlying
network is not available.
contabs.netwBMA proceeds by comparing
the intersecting subset of the proposed edges with a reference network
composed of regulator-genes pairs.
Edges that don't exist between any regulator and any target gene in the
reference network are used to complete the larger set P
of all possible edges for the comparison.
Only the subset of network edges that belong to P is considered.
There are issues in handling known edges hardcoded in the modeling,
for which a detailed explanation is given in the package vignette.
The contingency table for the the intersection of
reference with edges in
Uncovering regulatory relationships in yeast using
networkBMA Bioconductor package vignette.
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## Not run: data(vignette) dim(timeSeries) colnames(timeSeries) table(timeSeries$replicate) table(timeSeries$time) dim(reg.known) colnames(reg.known) dim(reg.prob) edges <- networkBMA(data = timeSeries[,-(1:2)], nTimePoints = length(unique(timeSeries$time)), prior.prob = reg.prob, known = reg.known) contabs.netwBMA( network = edges, reference = referenceNetwork, known = reg.known) ## End(Not run)
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