This is a class representation of the output of the ternary
network fitting algorithm implemented in the function tnetfit.
Arguments
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)<-).
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.