Get average activity difference matrix based on the number of true positives

This function finds all the TP values of the models given (e.g. 0,1,2,3) and generates every pairwise combination (e.g. the group matchings: (0,1), (1,3), etc.). Then, it uses the get_avg_activity_diff_based_on_tp_predictions function on each generated classification group matching, comparing thus all groups of models with different true positive (TP) values, while taking into account the given penalty factor and the number of models in each respective model group.

get_avg_activity_diff_mat_based_on_tp_predictions(
  models.synergies.tp,
  models.stable.state,
  penalty = 0
)

Arguments

models.synergies.tp	an integer vector of TP values. The names attribute must hold the models' names. Consider using the function calculate_models_synergies_tp.
models.stable.state	a data.frame (nxm) with n models and m nodes. The row names specify the models' names (same order as in the models.synergies.tp parameter) 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.
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.

Value

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.

See also

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_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()