ternaryPost-class: Ternary Network Posterior

In ternarynet: Ternary Network Estimation

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)

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