ternaryPost-class {ternarynet} | R Documentation

## Ternary Network Posterior

### Description

This is a class representation of the output of the ternary network posterior sampling algorithm implemented in the function `tnetpost`.

### Creating Objects

While one can create their own objects using the function `ternaryPost()`, this is highly discouraged. Typically this class is created by running the `tnetpost` function.

### Slots

`perturbationObj`:

  a matrix of perturbation experiments. Rows are genes and columns are experiments.

`steadyStateObj`:

  a matrix of steady gene expression observations from a perturbation experiment. Rows are genes and columns are experiments.

`geneNames`:

  a vector of gene names corresponding to the rows of the perturbationObj and steadyStateObj.

`experimentNames`:

  a vector of experiment names corresponding to the columns of the perturbationObj and steadyStateObj.

`scores`:

  the score of each sample

`degreeObjs`:

  the in-degree vector for each sample

`graphObjs`:

  the graph matrix for each sample

`tableObjs`:

  the table matrix for each sample

`inputParams`:

  the ternaryFitParameters object used

### Methods

All named elements can be accessed and set in the standard way (e.g. `scores(object)` and `scores(object)<-`).

### Author(s)

Matthew N. McCall and Anthony Almudevar

### See Also

`tnetfit`, `ternaryFitParameters-class`, `ternaryFit-class`. 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)
    tnfitObj <- tnetfit(ssObj, pObj)
    tnpostObj <- tnetpost(tnfitObj, mdelta=10, msample=10)
    class(tnpostObj)

[Package ternarynet version 1.32.0 Index]