Find the number of predictive models for every synergy subset

Use this function to find for each possible subset of drug combinations out of a given list of synergies, the number of models that predicted it given the models' predictions. So, if for example the set of synergies is this one: {'A-B','C-D','E-F'}, we want to know how many models predicted none of them, just the single subsets (e.g. the {'A-B'}), the two-element subsets (e.g. the {'A-B','C-D'}) and all 3 of them.

get_synergy_subset_stats(model.predictions, synergies)

Arguments

model.predictions	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).
synergies	a character vector with elements the synergistic drug combinations. Note that these synergies should be a subset of the column names of the model.predictions data.frame.

Value

an integer vector with elements the number of models the predicted each synergy subset. The names attribute has the names of each synergistic drug combination subset, which are the drug combinations comma separated (e.g. 'A-B,C-D').

Details

Note that if the synergies vector has more than 10-15 elements, then this function might take long time to execute even with an optimal implementation of count_models_that_predict_synergies.