predictAttractor: Predict the attractor(s) resulting from a given perturbation

Description Usage Arguments Value Author(s) See Also Examples

Description

This function computes the posterior probabilities of attractors reached for a given perturbation using the networks from a ternaryPost object.

Usage

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predictAttractor(tpost, perturbations, wildtype = TRUE, verbose = FALSE)

Arguments

tpost

a ternaryPost object

perturbations

a list with two elements: perturbed.genes and forced.states

wildtype

if TRUE, the wildtype attractors are summarized; if FALSE, the perturbed attractors are summarized.

verbose

if TRUE, periodic reports on progress are printed.

Value

The function returns a list with two elements: \ post.prob: the posterior probability of each attractor \ attractor.summary: a single vector of steady states based on the resulting attractor

Author(s)

Matthew N. McCall and Anthony Almudevar

See Also

Almudevar A, McCall MN, McMurray H, Land H (2011). Fitting Boolean Networks from Steady State Perturbation Data, Statistical Applications in Genetics and Molecular Biology, 10(1): Article 47.

Examples

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ssObj <- matrix(c(1,1,1,0,1,1,0,0,1),nrow=3)
pObj <- matrix(c(1,0,0,0,1,0,0,0,1),nrow=3)
rownames(ssObj) <- rownames(pObj) <- colnames(ssObj) <- colnames(pObj) <- c("Gene1","Gene2","Gene3")
tnfitObj <- tnetfit(ssObj, pObj)
tnpostObj <- tnetpost(tnfitObj, mdelta=10, msample=10)
predictAttractor(tnpostObj, list(perturbed.genes=c(1,2),forced.states=c(1,1)))

mccallm/ternarynet documentation built on May 7, 2019, 1:27 p.m.