R/diff.R
get_avg_activity_diff_based_on_synergy_set_cmp.Rd
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, taking into account the given penalty
factor and the number of models in the 'good' and 'bad' class respectively.
get_avg_activity_diff_based_on_synergy_set_cmp( synergy.set.str, synergy.subset.str, model.predictions, models.stable.state, penalty = 0 )
synergy.set.str | 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 | 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 |
model.predictions | a |
models.stable.state | a |
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. |
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 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_based_on_synergy_set_cmp()
,
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()