This function splits the models to 'good' and 'bad' based on the number of true positive predictions: num.high TPs (good) vs num.low TPs (bad). Then, for each network node, it finds the node's average activity in each of the two classes (a value in the [0,1] interval) and then subtracts the 'bad' average activity value from the good' one.
get_avg_activity_diff_based_on_tp_predictions(models, models.synergies.tp,
models.stable.state, num.low, num.high)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.
a matrix (nxm) with n models and m nodes. The row
names of the matrix specify the models' names (same order as in the models
parameter) whereas the column names specify the name of the network nodes
(gene, proteins, etc.). Possible values for each model-node element
are either 0 (inactive node) or 1 (active node).
integer. The number of true positives representing the 'bad' model class.
integer. The number of true positives representing the 'good'
model class. This number has to be strictly higher than num.low.
a numeric vector with values in the [-1,1] interval (minimum and maximum possible average difference) and with the names attribute representing the name of the nodes.
So, if a node has a value close to -1 it means that on average, this node is more inhibited in the 'good' models compared to the 'bad' ones while a value closer to 1 means that the node is more activated in the 'good' models. A value closer to 0 indicates that the activity of that node 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 good models.
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_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,
get_avg_link_operator_diff_mat_based_on_tp_predictions