This function splits the models to 'good' and 'bad' based on the predictions
of two different synergy sets, one of them being a subset of the other.
The 'good' models are those that predict the synergy.set.str
(e.g. "A-B,A-C,B-C") while the 'bad' models are those that predict the
synergy.subset.str (e.g. "A-B,B-C"). Then, for each network node,
the function finds the node's average activity in each of the two classes
(a value in the [0,1] interval) and then subtracts the bad class average
activity value from the good one.
get_avg_activity_diff_based_on_synergy_set_cmp(synergy.set.str,
synergy.subset.str, model.predictions, models.stable.state)a string of drug combinations, comma-separated. The
number of the specified combinations must be larger than the ones defined
in the synergy.subset.str parameter. They also must be included in the
tested drug combinations, i.e. the columns of the model.predictions
parameter.
a string of drug combinations, comma-separated.
There must be at least one combination defined and all of them should also
be included in the synergy.set.str parameter.
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)
a 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 (inactive node) or 1 (active node).
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 models that predicted the extra
synergy(-ies) that are included in the synergy.set.str but not in the
synergy.subset.str, whereas a value closer to 1 means that the node is
more activated in these models. These nodes are potential
biomarkers because their activity state can influence the prediction
performance of a model and make it predict the extra synergy(-ies).
A value closer to 0 indicates that the activity of that
node is not so much different between the models that predicted the
synergy set and those that predicted it's subset, so it won't not be a node
of interest when searching for potential biomarkers for the extra synergy(-ies).
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_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,
get_avg_link_operator_diff_mat_based_on_tp_predictions