Run discovery analysis — run_discovery

run_discovery_analysis() runs the discovery analysis. The discovery analysis involves applying sceptre to analyze discovery pairs, or target-response pairs whose association status we do not know but seek to learn. Identifying associations among the discovery pairs is the primary objective of the single-cell CRISPR screen analysis. See Chapter 6 of the manual for more detailed information about this function.

Usage

run_discovery_analysis(
  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. parallel = TRUE is recommended only on Mac; it is not supported on Windows and may behave unreliably on Linux clusters.
n_processors: (optional; default "auto") an integer specifying the number of processors to use if parallel is set to TRUE. The default, "auto", uses half the physical cores. The fraction may be tuned via the parallelly.availableCores.fraction R option.
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 discovery analysis has been carried out

Examples

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
discovery_pairs <- construct_cis_pairs(sceptre_object)
sceptre_object <- sceptre_object |>
  set_analysis_parameters(
    side = "left",
    resampling_mechanism = "permutations",
    discovery_pairs = discovery_pairs
  ) |>
  assign_grnas(method = "thresholding") |>
  run_qc() |>
  run_discovery_analysis()
#> Note: If you are on a Mac laptop or desktop, consider setting `parallel = TRUE` to improve speed. Otherwise, keep `parallel = FALSE`.
#> The calibration check (`run_calibration_check()`) should be run before the discovery analysis.
#> Generating permutation resamples.
#>  ✓
#> Analyzing pairs containing response ENSG00000100218 (1 of 85)
#> Analyzing pairs containing response ENSG00000099958 (5 of 85)
#> Analyzing pairs containing response ENSG00000211638 (10 of 85)
#> Analyzing pairs containing response ENSG00000274422 (15 of 85)
#> Analyzing pairs containing response ENSG00000133475 (20 of 85)
#> Analyzing pairs containing response ENSG00000236003 (25 of 85)
#> Analyzing pairs containing response ENSG00000211661 (30 of 85)
#> Analyzing pairs containing response ENSG00000225783 (35 of 85)
#> Analyzing pairs containing response ENSG00000169548 (40 of 85)
#> Analyzing pairs containing response ENSG00000253631 (45 of 85)
#> Analyzing pairs containing response ENSG00000286941 (50 of 85)
#> Analyzing pairs containing response ENSG00000286365 (55 of 85)
#> Analyzing pairs containing response ENSG00000100325 (60 of 85)
#> Analyzing pairs containing response ENSG00000099956 (65 of 85)
#> Analyzing pairs containing response ENSG00000100276 (70 of 85)
#> Analyzing pairs containing response ENSG00000234630 (75 of 85)
#> Analyzing pairs containing response ENSG00000253963 (80 of 85)
#> Analyzing pairs containing response ENSG00000100027 (85 of 85)