Use this function to discover synergy biomarkers, i.e. nodes whose activity and/or boolean equation parameterization (link operator) affect the manifestation of synergies in the models. Models are classified based on whether they predict or not each of the predicted synergies.
biomarker_synergy_analysis(model.predictions, models.stable.state,
models.link.operator = NULL, observed.synergies, threshold)a data.frame object with rows the models and
columns the drug combinations. Possible values for each model-drug combination
element are either 0 (no synergy predicted), 1 (synergy was
predicted) or NA (couldn't find stable states in either the drug
combination inhibited model or in any of the two single-drug inhibited models).
a matrix (nxm) with n models and m nodes. The row
names of the matrix specify the models' names whereas the column names
specify the name of the network nodes (gene, proteins, etc.).
Possible values for each model-node element
are either 0 (inactive node) or 1 (active node). Note that the
rows (models) have to be in the same order as in the model.predictions
parameter.
a matrix (nxm) with n models and m nodes. The row names of the matrix specify the models' names whereas the column names specify the name of the network nodes (gene, proteins, etc.). Possible values for each model-node element are either 0 (AND NOT link operator), 1 (OR NOT link operator) or 0.5 if the node is not targeted by both activating and inhibiting regulators (no link operator). Default value: NULL (no analysis on the models parameterization regarding the mutation of the boolean equation link operator will be done).
a character vector with elements the names of the
drug combinations that were found as synergistic. This should be a subset of
the tested drug combinations, that is the column names of the model.predictions
parameter.
numeric. A number in the [0,1] interval, above which (or below its negative value) a biomarker will be registered in the returned result. Values closer to 1 translate to a more strict threshold and thus less biomarkers are found.
a list with various elements:
observed.model.predictions: the part of the model.predictions
data that includes the observed.synergies.
unobserved.model.predictions: the complementary part of the
model.predictions data that does not include the observed.synergies
predicted.synergies: a character vector of the synergies (drug
combination names) that were predicted by at least one of the models
in the dataset.
synergy.subset.stats: an integer vector with elements the number
of models the predicted each observed synergy subset.
diff.state.synergies.mat: a matrix whose rows are
vectors of average node activity state differences between two
groups of models where the classification for each individual row was based
on the prediction or not of a specific synergistic drug combination. The
row names are the predicted synergies, one per row, while the columns
represent the network's node names. Values are in the [-1,1] interval.
activity.biomarkers: a data.frame object with rows
the predicted synergies and columns the nodes (column names of the
models.stable.states matrix). Possible values for each
synergy-node element are either 1 (active state
biomarker), -1 (inhibited state biomarker) or 0 (not
a biomarker) for the given threshold value.
diff.link.synergies.mat: a matrix whose rows are
vectors of average node link operator differences between two
groups of models where the classification for each individual row was
based on the prediction or not of a specific synergistic drug combination.
The row names are the predicted synergies, one per row, while the columns
represent the network's node names. Values are in the [-1,1] interval.
link.operator.biomarkers: a data.frame object with rows
the predicted synergies and columns the nodes (column names of the
models.link.operator matrix). Possible values for each
synergy-node element are either 1 (OR link operator
biomarker), -1 (AND link operator biomarker) or 0 (not
a biomarker) for the given threshold value.
Other general analysis functions: biomarker_mcc_analysis,
biomarker_tp_analysis