Get average link operator difference matrix based on MCC clustering

This function uses the get_avg_activity_diff_mat_based_on_mcc_clustering 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_mcc_clustering(
  models.mcc,
  models.link.operator,
  num.of.mcc.classes,
  penalty = 0
)

Arguments

models.mcc	a numeric vector of Matthews Correlation Coefficient (MCC) scores, one for each model. The names attribute holds the models' names. Can be the result of using the function calculate_models_mcc.
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 models.mcc 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).
num.of.mcc.classes	numeric. A positive integer larger than 2 that signifies the number of mcc classes (groups) that we should split the models MCC values.
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 link operator differences between two groups of models where the classification was based on the models' MCC values. Rows represent the different classification group matchings, e.g. (1,2) means the models that belonged to the 1st group of MCC values vs the models that belonged to the 2nd group. The columns represent the network's node names. Values are in the [-1,1] interval.

Details

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 'good' models compared to the 'bad' ones while a value closer to 1 means that the node's boolean equation has mostly the OR NOT link operator in the 'good' models. A value closer to 0 indicates that the link operator in the node's boolean equation is not so much different between the 'good' and 'bad' models and so it won't not be a node of interest when searching for indicators of better performance (higher average MCC value) in the parameterization of the good models (the boolean equations). A value exactly equal to 0 can also mean that this node didn't not have a link operator in its boolean equation, again making it a non-important indicator of difference in model performance.

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_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_specific_synergy_prediction(), get_avg_link_operator_diff_mat_based_on_tp_predictions()