This function uses the get_avg_activity_diff_mat_based_on_tp_predictions
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_tp_predictions(models,
models.synergies.tp, models.link.operator)character vector. The model names.
an integer vector of TP values. The names
attribute holds the models' names and have to be in the same order as in the
models parameter.
matrix (nxm) with n models and m nodes. The row names of the matrix specify the models' names whereas the column names specify the name of 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).
a matrix whose rows are vectors of average node link operator
differences between two groups of models based on some kind of classification
(e.g. number of TP predictions) and whose names are set in the rownames
attribute of the matrix (usually denoting the different classification
groups, e.g. (1,2) means the models that predicted 1 TP synergy vs the models
that predicted 2 TP synergies, if the classification is done by number of TP
predictions). 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 '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 number of true positives) 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.
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_mat_based_on_mcc_clustering,
get_avg_link_operator_diff_mat_based_on_specific_synergy_prediction