ternaryFit-class: Ternary Network Fit

In ternarynet: Ternary Network Estimation

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)

ternarynet documentation built on Nov. 8, 2020, 7 p.m.