tests/testthat/test-roxytest-testexamples-best_practice.R

In ShixiangWang/sigminer: Extract, Analyze and Visualize Mutational Signatures for Genomic Variations

# Generated by roxytest: do not edit by hand!

# File R/best_practice.R: @testexamples

test_that("[unknown alias] @ L210", {
  
  data("simulated_catalogs")
  
  # Here I reduce the values for n_bootstrap and n_nmf_run
  # for reducing the run time.
  # In practice, you should keep default or increase the values
  # for better estimation.
  #
  # The input data here is simulated from 10 mutational signatures
  
  # e1 <- bp_extract_signatures(
  #   t(simulated_catalogs$set1),
  #   range = 8:12,
  #   n_bootstrap = 5,
  #   n_nmf_run = 10
  # )
  #
  # To avoid computation in examples,
  # Here just load the result
  # (e1$signature and e1$exposure set to NA to reduce package size)
  load(system.file("extdata", "e1.RData", package = "sigminer"))
  
  
  # See the survey for different signature numbers
  # The suggested solution is marked as red dot
  # with highest integrated score.
  p1 <- bp_show_survey(e1)
  p1
  # You can also exclude plotting and highlighting the score
  p2 <- bp_show_survey(e1, add_score = FALSE)
  p2
  
  # You can also plot a simplified version
  p3 <- bp_show_survey2(e1, highlight = 10)
  p3
  
  # Obtain the suggested solution from extraction result
  obj_suggested <- bp_get_sig_obj(e1, e1$suggested)
  obj_suggested
  # If you think the suggested signature number is not right
  # Just pick up the solution you want
  obj_s8 <- bp_get_sig_obj(e1, 8)
  
  # Track the reconstructed profile similarity
  rec_sim <- get_sig_rec_similarity(obj_s8, t(simulated_catalogs$set1))
  rec_sim
  
  # After extraction, you can assign the signatures
  # to reference COSMIC signatures
  # More see ?get_sig_similarity
  sim <- get_sig_similarity(obj_suggested)
  # Visualize the match result
  if (require(pheatmap)) {
    pheatmap::pheatmap(sim$similarity)
  }
  
  # You already got the activities of signatures
  # in obj_suggested, however, you can still
  # try to optimize the result.
  # NOTE: the optimization step may not truly optimize the result!
  expo <- bp_attribute_activity(e1, return_class = "data.table")
  expo$abs_activity
  
  
  
  expect_is(e1, "ExtractionResult")
  expect_is(p1, "ggplot")
  expect_is(p2, "ggplot")
  expect_is(p2, "ggplot")
  expect_is(obj_suggested, "Signature")
  expect_is(obj_s8, "Signature")
  expect_is(sim, "list")
  expect_is(rec_sim, "data.table")
  expect_is(expo, "list")
})