biomarker_tp_analysis: Biomarker analysis based on TP model classification
In emba: Ensemble Boolean Model Biomarker Analysis

Description Usage Arguments Value See Also

Use this function to perform a full biomarker analysis on an ensemble boolean model dataset where the model classification is based on the number of true positive (TP) predictions. This analysis enables the discovery of performance biomarkers, nodes whose activity and/or boolean model parameterization (link operator) affects the prediction performance of the models (as measured by the number of TPs).

biomarker_tp_analysis(
  model.predictions,
  models.stable.state,
  models.link.operator = NULL,
  observed.synergies,
  threshold,
  penalty = 0.1
)

`model.predictions`	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).
`models.stable.state`	a `data.frame` (nxm) with n models and m nodes. The row names specify the models' names whereas the column names specify the network nodes (gene, proteins, etc.). Possible values for each model-node element can be between 0 (inactive node) and 1 (active node) inclusive. Note that the rows (models) have to be in the same order as in the `model.predictions` parameter.
`models.link.operator`	a `data.frame` (nxm) with n models and m nodes. The row names specify the models' names (same order as in the `model.predictions` parameter) whereas the column names specify 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).
`observed.synergies`	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.
`threshold`	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.
`penalty`	value between 0 and 1 (inclusive). A value of 0 means no penalty and a value of 1 is the strickest possible penalty. Default value is 0.1. This penalty is used as part of a weighted term to the difference in a value of interest (e.g. activity or link operator difference) between two group of models, to account for the difference in the number of models from each respective model group.

a list with various elements:

predicted.synergies: a character vector of the synergies (drug combination names) that were predicted by at least one of the models in the dataset.
models.synergies.tp: an integer vector of true positive (TP) values, one for each model.
diff.tp.mat: a matrix whose rows are vectors of average node activity state differences between two groups of models where the classification was based on the number of true positive predictions. Rows represent the different classification group matchings, e.g. (1,2) means the models that predicted 1 TP synergy vs the models that predicted 2 TP synergies and the columns represent the network's node names. Values are in the [-1,1] interval.
biomarkers.tp.active: a character vector whose elements are the names of the active state biomarkers. These nodes appear as more active in the better performance models.
biomarkers.tp.inhibited: a character vector whose elements are the names of the inhibited state biomarkers. These nodes appear as more inhibited in the better performance models.
diff.link.tp.mat: a matrix whose rows are vectors of average node link operator differences between two groups of models where the classification was based on the number of true positive predictions. Rows represent the different classification group matchings, e.g. (1,2) means the models that predicted 1 TP synergy vs the models that predicted 2 TP synergies and the columns represent the network's node names. Values are in the [-1,1] interval.
biomarkers.tp.or: a character vector whose elements are the names of the OR link operator biomarkers. These nodes have mostly the OR link operator in their respective boolean equations in the better performance models.
biomarkers.tp.and: a character vector whose elements are the names of the AND link operator biomarkers. These nodes have mostly the AND link operator in their respective boolean equations in the better performance models.