reflect_pipeline: Run an End-to-end REFLECT Pipeline
In korkutlab/reflect: REcurrent Features LEveraged for Combination Therapy

Description Usage Arguments Value Examples

Given an expression/alteration matrix and annotations of samples and features, the REFLECT pipeline nominates co-altered, recurrent, actionable combination targets. The tunning parameter wbound is selected based on a permuation approach. Note this pipeline may take a long time because obtaining a tunning parameter through gap statitic may be computationally expensive.

reflect_pipeline(
  mat_value,
  df_sample,
  df_feature = NULL,
  dissimilarity = c("squared.distance", "absolute.value"),
  wbounds = NULL,
  nperms = 10,
  min_number_features = 10,
  pval_threshold = 0.05
)

`mat_value`	A matrix of expression/alteration with samples as rows and features as columns.
`df_sample`	A data frame of samples including tumor types and stratification status. It must contain columns: SampleID, TumorType, Stratification.
`df_feature`	A data frame that annotates function scores and actionabilities of features. It must contains columns: Feature, FunctionScore, IsActionable. If df_feature = NULL, both 1 (activating) and -1 (inhibiting) are considered as function scores for each feature, and all features are considered as being actionable.
`dissimilarity`	A string for the type of dissimilarity, either "squared.distance" or "absolute.value". Default "squared.distance".
`wbounds`	The sequence of tuning parameters to consider. If NULL, then a default sequence seq(1.1, sqrt(ncol(mat_value)), 100) will be used. If non-null, should be greater than 1.
`nperms`	The number of permutations to perform. Default 10.
`min_number_features`	The minimal number of features that the best wbound could generate. Only wbounds that generates more than this number of features can considered. Default 10.
`pval_threshold`	The threshold of P value below which are considered statistically significant. Default 0.05.

`gapstat_bestwbound`	Gap statistic profile and the value of best tunning parameter wbound. See detial in function get_best_wbound.
`shc`	An object of class hclust which describes the tree produced by the clustering process. See detial in function sparse_hclust.
`mat_recur_pval`	A matrix of recurrence P values. See detial in function get_recur_pval.
`recur_actionable`	An object for rcurrent and acitionable features. See detial in function get_recur_actionable_features.
`df_coaltered_targets`	A data frame of samples that have both stratification status and recurrent/actionable features for each sample. See detial in function get_coaltered_targets.

library(reflect)
mat_value <- egfr_data$mat_value
df_sample <- egfr_data$df_sample
df_feature <- egfr_data$df_feature
res <- reflect_pipeline(mat_value, df_sample, df_feature)