Getting Started • CaDrA.shiny

(1) Installation

Using devtools package


library(devtools)
devtools::install_github("montilab/CaDrA")

Using BiocManager package


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("CaDrA")

(2) Quickstart

library(CaDrA)
library(SummarizedExperiment)

(3) CaDrA Query of BRCA YAP/TAZ Activity

(i) Load & Format Data Inputs


## Read in BRCA GISTIC+Mutation object
data(BRCA_GISTIC_MUT_SIG)
eset_mut_scna <- BRCA_GISTIC_MUT_SIG

## Read in input score
data(TAZYAP_BRCA_ACTIVITY)
input_score <- TAZYAP_BRCA_ACTIVITY

## Samples to keep based on the overlap between the two inputs
overlap <- intersect(names(input_score), colnames(eset_mut_scna))
eset_mut_scna <- eset_mut_scna[,overlap]
input_score <- input_score[overlap]

## Binarize FS to only have 0's and 1's
assay(eset_mut_scna)[assay(eset_mut_scna) > 1] <- 1.0

## Pre-filter FS based on occurrence frequency
eset_mut_scna_flt <- CaDrA::prefilter_data(
  FS = eset_mut_scna,
  max_cutoff = 0.6,  # max event frequency (60%)
  min_cutoff = 0.03  # min event frequency (3%)
)

(ii) Run CaDrA

Here, we repeat the candidate search starting from each of the top ‘N’ features and report the combined results as a heatmap (to summarize the number of times each feature is selected across repeated runs).

IMPORTANT NOTE: The legacy function topn_eval() is equivalent to the new recommended candidate_search() function


topn_res <- CaDrA::candidate_search(
  FS = eset_mut_scna_flt,
  input_score = input_score,
  method = "ks_pval",          # Use Kolmogorow-Smirnow scoring function 
  method_alternative = "less", # Use one-sided hypothesis testing
  weights = NULL,              # If weights is provided, perform a weighted-KS test
  search_method = "both",      # Apply both forward and backward search
  top_N = 7,                   # Evaluate top 7 starting points for each search
  max_size = 7,                # Maximum size a meta-feature matrix can extend to
  do_plot = FALSE,             # Plot after finding the best features
  best_score_only = FALSE      # Return all results from the search
)

(iii) Visualize Best Results


## Fetch the meta-feature set corresponding to its best scores over top N features searches
topn_best_meta <- CaDrA::topn_best(topn_res)

# Visualize the best results with the meta-feature plot
CaDrA::meta_plot(topn_best_list = topn_best_meta, input_score_label = "YAP/TAZ Activity")

(iv) Summarize Top N Results


# Evaluate results across top N features you started from
CaDrA::topn_plot(topn_res)