predictAttractor: Predict the attractor(s) resulting from a given perturbation In mccallm/ternarynet: Ternary Network Estimation

Description

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

Usage

 `1` ```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

 ```1 2 3 4 5 6``` ```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))) ```