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Run calibration check

Source: R/s4_analysis_functs_2.R
run_calibration_check.Rd

run_calibration_check() runs the calibration check. The calibration check involves applying sceptre to analyze negative control target-response pairs --- pairs for which we know there is no association between the target and response --- to ensure control of the false discovery rate. The calibration check enables us to verify that the discovery set that sceptre ultimately produces is not contaminated by excess false positives. See Chapter 5 of the manual for more detailed information about this function.

Usage

run_calibration_check(
  sceptre_object,
  n_calibration_pairs = NULL,
  calibration_group_size = NULL,
  print_progress = TRUE,
  parallel = FALSE,
  n_processors = "auto",
  log_dir = tempdir(),
  output_amount = 1
)

Arguments

sceptre_object

a sceptre_object

n_calibration_pairs

(optional) the number of negative control pairs to construct and test for association

calibration_group_size

(optional) the number of negative control gRNAs to randomly assemble to form each negative control target

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)

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.

Value

an updated sceptre_object in which the calibration 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, run calibration check
sceptre_object <- sceptre_object |>
  set_analysis_parameters(
    side = "left",
    resampling_mechanism = "permutations"
  ) |>
  assign_grnas(method = "thresholding") |>
  run_qc() |>
  run_calibration_check(
    n_calibration_pairs = 500,
    calibration_group_size = 2,
    parallel = TRUE,
    n_processors = 2
  )
#> Constructing negative control pairs. ✓
#> Generating permutation resamples. ✓
#> Running calibration_check in parallel. Change directories to /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpHhxNRw/sceptre_logs/ and view the files calibration_check_*.out for progress updates.
#>  ✓
#>