get_avg_link_operator_diff_mat_based_on_specific_synergy_prediction: Get average link operator difference matrix based on specific...
In emba: Ensemble Boolean Model Biomarker Analysis

Description Usage Arguments Value Details See Also

This function uses the get_avg_activity_diff_mat_based_on_specific_synergy_prediction function with the parameter models.link.operator as input in the place of models.stable.state, since the two matrices representing the two inputs have the same data format (rows represent models, columns represent nodes, and each value is a number in the [0,1] interval).

get_avg_link_operator_diff_mat_based_on_specific_synergy_prediction(
  model.predictions,
  models.link.operator,
  predicted.synergies,
  penalty = 0
)

`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.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).
`predicted.synergies`	a character vector of the synergies (drug combination names) that were predicted by at least one of the models in the dataset. It must be a subset of the column names (the drug combinations) of the `model.predictions` object.
`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. 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 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.

So, if a node has a value close to -1 it means that on average, this node's boolean equation has the AND NOT link operator in the models that predicted the specified synergy while a value closer to 1 means that the node's boolean equation has mostly the OR NOT link operator in these models. A value closer to 0 indicates that the link operator in the node's boolean equation is not so much different between the models that predicted the synergy and those that did not and so it won't not be a node of interest when searching for synergy biomarkers - nodes whose parameterization (value of the link operator) affects the manifestation of synergy. A value exactly equal to 0 can also mean that this node didn't not have a link operator in its boolean equation (making it thus a non-important node with regard to the parameterization).

get_vector_diff

Other average data difference functions: get_avg_activity_diff_based_on_mcc_clustering(), get_avg_activity_diff_based_on_specific_synergy_prediction(), get_avg_activity_diff_based_on_synergy_set_cmp(), get_avg_activity_diff_based_on_tp_predictions(), get_avg_activity_diff_mat_based_on_mcc_clustering(), get_avg_activity_diff_mat_based_on_specific_synergy_prediction(), get_avg_activity_diff_mat_based_on_tp_predictions(), get_avg_link_operator_diff_based_on_synergy_set_cmp(), get_avg_link_operator_diff_mat_based_on_mcc_clustering(), get_avg_link_operator_diff_mat_based_on_tp_predictions()