ternaryFit-class {ternarynet} | R Documentation

## Ternary Network Fit

### Description

This is a class representation of the output of the ternary network fitting algorithm implemented in the function `tnetfit`.

### Creating Objects

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

`degreeObjMin`:

  a vector containing the in-degree of each node in the fit achieving the minimum score

`graphObjMin`:

  a matrix containing the parents of each node in the fit achieving the minimum score

`tableObjMin`:

  a matrix containing the table in the fit achieving the minimum score

`newScore`:

  the most recent score

`minScore`:

  the minimum score

`finalTemperature`:

  the final value of the temperature parameter

`traces`:

  a dataframe contain the traces for 4 parameters

`stageCount`:

  the number of stages

`xSeed`:

  the random seed.

`inputParams`:

  the ternaryFitParameters object used.

### Methods

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

### Author(s)

Matthew N. McCall and Anthony Almudevar

### See Also

`tnetpost`, `ternaryFitParameters-class`, `ternaryPost-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)
    class(tnfitObj)

[Package ternarynet version 1.36.0 Index]