get_avg_activity_diff_mat_based_on_specific_synergy_prediction: Get average activity difference matrix based on specific...
In emba: Ensemble Boolean Model Biomarker Analysis

Description Usage Arguments Value See Also

This function uses the get_avg_activity_diff_based_on_specific_synergy_prediction function on a vector of drug combinations that were observed as synergistic (e.g. by experiments) but also found as such by at least one of the models (these drug combinations are the predicted.synergies).

get_avg_activity_diff_mat_based_on_specific_synergy_prediction(
  model.predictions,
  models.stable.state,
  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.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.
`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 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.

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_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_specific_synergy_prediction(), get_avg_link_operator_diff_mat_based_on_tp_predictions()