predictAttractor {ternarynet} | R Documentation

## Predict the attractor(s) resulting from a given perturbation

### Description

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

### Usage

    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

    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)))

[Package ternarynet version 1.34.0 Index]