Plot run power check — plot_run_power

plot_run_power_check() creates a visualization of the outcome of the power check analysis. Each point in the plot corresponds to a target-response pair, with positive control pairs in the left column and negative control pairs in the right column. The vertical axis indicates the p-value of a given pair; smaller (i.e., more significant) p-values are positioned higher along this axis (p-values truncated at clip_to for visualization). The positive control p-values should be small, and in particular, smaller than the negative control p-values.

Usage

plot_run_power_check(
  sceptre_object,
  point_size = 1,
  transparency = 0.8,
  clip_to = 1e-20
)

Arguments

sceptre_object: a sceptre_object that has had run_power_check() called on it
point_size: (optional; default 1) the size of the individual points in the plot
transparency: (optional; default 0.8) the transparency of the individual points in the plot
clip_to: (optional; default 1e-20) p-values smaller than this value are set to clip_to for better visualization. If clip_to=0 is used then no clipping is done.

Value

a single ggplot2 plot

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
)
positive_control_pairs <- construct_positive_control_pairs(sceptre_object)
sceptre_object |>
  set_analysis_parameters(
    side = "left",
    positive_control_pairs = positive_control_pairs,
    resampling_mechanism = "permutations",
  ) |>
  assign_grnas(method = "thresholding") |>
  run_qc() |>
  run_calibration_check(
    n_calibration_pairs = 500,
    calibration_group_size = 2
  ) |>
  run_power_check() |>
  plot_run_power_check()
#> Note: If you are on a Mac laptop or desktop, consider setting `parallel = TRUE` to improve speed. Otherwise, keep `parallel = FALSE`.
#> Constructing negative control pairs.
#>  ✓
#> Generating permutation resamples.
#>  ✓
#> Analyzing pairs containing response ENSG00000253631 (1 of 96)
#> Analyzing pairs containing response ENSG00000100053 (5 of 96)
#> Analyzing pairs containing response ENSG00000100325 (10 of 96)
#> Analyzing pairs containing response ENSG00000253963 (15 of 96)
#> Analyzing pairs containing response ENSG00000100314 (20 of 96)
#> Analyzing pairs containing response ENSG00000177993 (25 of 96)
#> Analyzing pairs containing response ENSG00000099956 (30 of 96)
#> Analyzing pairs containing response ENSG00000253920 (35 of 96)
#> Analyzing pairs containing response ENSG00000203280 (40 of 96)
#> Analyzing pairs containing response ENSG00000187792 (45 of 96)
#> Analyzing pairs containing response ENSG00000211666 (50 of 96)
#> Analyzing pairs containing response ENSG00000236611 (55 of 96)
#> Analyzing pairs containing response ENSG00000253546 (60 of 96)
#> Analyzing pairs containing response ENSG00000099917 (65 of 96)
#> Analyzing pairs containing response ENSG00000253889 (70 of 96)
#> Analyzing pairs containing response ENSG00000099889 (75 of 96)
#> Analyzing pairs containing response ENSG00000241973 (80 of 96)
#> Analyzing pairs containing response ENSG00000225783 (85 of 96)
#> Analyzing pairs containing response ENSG00000279548 (90 of 96)
#> Analyzing pairs containing response ENSG00000100068 (95 of 96)
#> Note: If you are on a Mac laptop or desktop, consider setting `parallel = TRUE` to improve speed. Otherwise, keep `parallel = FALSE`.
#> Generating permutation resamples.
#>  ✓
#> Analyzing pairs containing response ENSG00000224277 (1 of 5)
#> Analyzing pairs containing response ENSG00000226772 (5 of 5)