This is a class representation of the output of the ternary
network posterior sampling algorithm implemented in the function
While one can create their own objects using the function
ternaryPost(), this is highly discouraged. Typically this class
is created by running the
a matrix of perturbation experiments. Rows are genes and columns are experiments.
a matrix of steady gene expression observations from a perturbation experiment. Rows are genes and columns are experiments.
a vector of gene names corresponding to the rows of the perturbationObj and steadyStateObj.
a vector of experiment names corresponding to the columns of the perturbationObj and steadyStateObj.
the score of each sample
the in-degree vector for each sample
the graph matrix for each sample
the table matrix for each sample
the ternaryFitParameters object used
All named elements can be accessed and set in the standard way
Matthew N. McCall and Anthony Almudevar
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.
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