Get the average activity difference based on MCC clustering

This function splits the models to 'good' and 'bad' based on an MCC value clustering method: class.id.high denotes the group id with the higher MCC values (good model group) vs class.id.low which denotes the group id with the lower MCC values (bad model group). 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 each respective model group.

get_avg_activity_diff_based_on_mcc_clustering(
  models.mcc,
  models.stable.state,
  mcc.class.ids,
  models.cluster.ids,
  class.id.low,
  class.id.high,
  penalty = 0
)

Arguments

models.mcc	a numeric vector of Matthews Correlation Coefficient (MCC) scores, one for each model. The names attribute holds the models' names. Can be the result of using the function calculate_models_mcc.
models.stable.state	a data.frame (nxm) with n models and m nodes. The row names specify the models' names whereas the column names specify the network nodes (gene, proteins, etc.). Possible values for each model-node element can be between 0 (inactive node) and 1 (active node) inclusive.
mcc.class.ids	a numeric vector of group/class ids starting from 1, e.g. c(1,2,3) (3 MCC classes).
models.cluster.ids	a numeric vector of cluster ids assigned to each model. It is the result of using Ckmeans.1d.dp with input the vector of the models' MCC values.
class.id.low	integer. This number specifies the MCC class id of the 'bad' models.
class.id.high	integer. This number specifies the MCC class id of the 'good' models and needs to be strictly higher than class.id.low.
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.

Value

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.

Details

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 MCC score/class) in the good models.

See also

get_vector_diff

Other average data difference functions: 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_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()