get_coaltered_targets: Get Co-altered Targets from Stratification Status and...
In korkutlab/reflect: REcurrent Features LEveraged for Combination Therapy

Description Usage Arguments Value Examples

For each sample, given its stratification status and recurrent/actionable features identified by REFLECT, it combines them to form co-altered targets towards combination therapies.

1	get_coaltered_targets(df_sample, df_recur_actionable)

`df_sample`	A data frame of samples including tumor types and stratification status. It must contain columns: SampleID, TumorType, Stratification.
`df_recur_actionable`	A data frame of samples having recurrent and actionable features. It must contain columns: SampleID, Feature, Feature_Value, Feature_Recur_Pval. It can be generated by function get_recur_actionable_features.

A data frame of samples that have both stratification status and recurrent/actionable features for each sample. It contains columns: SampleID, TumorType, Stratification, Feature, Feature_Value, Feature_Recur_Pval.

library(reflect)
mat_value <- egfr_data$mat_value
wbound <- 2.0
mat_value_clustered <- sparse_hclust(mat_value, wbound)$mat_value_clustered

df_feature <- egfr_data$df_feature
mat_recur_pval <- get_recur_pval(mat_value_clustered, df_feature)

recur_actionable <- get_recur_actionable_features(mat_value, mat_recur_pval)
df_recur_actionable <- recur_actionable$df_recur_actionable

df_sample <- egfr_data$df_sample
df_coaltered_targets <- get_coaltered_targets(df_sample, df_recur_actionable)