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Run power check

Source: R/s4_analysis_functs_2.R
run_power_check.Rd

run_power_check() runs the power check. The power check entails applying sceptre to analyze positive control pairs --- pairs for which we know there is an association between the target and the response --- to ensure that sceptre is capable of detecting true associations. See Chapter 6 of the manual for more detailed information about this function.

Usage

run_power_check(
  sceptre_object,
  output_amount = 1,
  print_progress = TRUE,
  parallel = FALSE,
  n_processors = "auto",
  log_dir = tempdir()
)

Arguments

sceptre_object

a sceptre_object

output_amount

(optional; default 1) an integer taking values 1, 2, or 3 specifying the amount of information to return. 1 returns the least amount of information and 3 the most.

print_progress

(optional; default TRUE) a logical indicating whether to print progress updates

parallel

(optional; default FALSE) a logical indicating whether to run the function in parallel

n_processors

(optional; default "auto") an integer specifying the number of processors to use if parallel is set to TRUE. The default, "auto", automatically detects the number of processors available on the machine.

log_dir

(optional; default tempdir()) a string indicating the directory in which to write the log files (ignored if parallel = FALSE)

Value

an updated sceptre_object in which the power check has been carried out

Examples

library(sceptredata)
data(highmoi_example_data)
data(grna_target_data_frame_highmoi)
# import data
sceptre_object <- import_data(
  response_matrix = highmoi_example_data$response_matrix,
  grna_matrix = highmoi_example_data$grna_matrix,
  grna_target_data_frame = grna_target_data_frame_highmoi,
  moi = "high",
  extra_covariates = highmoi_example_data$extra_covariates,
  response_names = highmoi_example_data$gene_names
)

# set analysis parameters, assign grnas, run qc
positive_control_pairs <- construct_positive_control_pairs(sceptre_object)
sceptre_object <- sceptre_object |>
  set_analysis_parameters(
    side = "left",
    resampling_mechanism = "permutations",
    positive_control_pairs = positive_control_pairs
  ) |>
  assign_grnas(method = "thresholding") |>
  run_qc() |>
  run_power_check()
#> Note: If you are on a Mac laptop or desktop, consider setting `parallel = TRUE` to improve speed. Otherwise, keep `parallel = FALSE`.
#> 
#> Warning: The calibration check (`run_calibration_check()`) should be run before the power check.
#> 
#> Generating permutation resamples. ✓
#> Analyzing pairs containing response ENSG00000104131 (1 of 10)
#> Analyzing pairs containing response ENSG00000147669 (5 of 10)
#> Analyzing pairs containing response ENSG00000176890 (10 of 10)