This is a class representation of the output of the ternary
network fitting algorithm implemented in the function
While one can create their own objects using the function
ternaryFit(), 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.
a vector containing the in-degree of each node in the fit achieving the minimum score
a matrix containing the parents of each node in the fit achieving the minimum score
a matrix containing the table in the fit achieving the minimum score
the most recent score
the minimum score
the final value of the temperature parameter
a dataframe contain the traces for 4 parameters
the number of stages
the random seed.
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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