Plot run calibration check — plot_run_calibration

plot_run_calibration_check() creates a visualization of the outcome of the calibration check. The visualization consists of four panels, which we describe below.

The upper left panel is a QQ plot of the p-values plotted on an untransformed scale. The p-values ideally should lie along the diagonal line, indicating uniformity of the p-values in the bulk of the distribution.
The upper right panel is a QQ plot of the p-values plotted on a negative log-10 transformed scale. The p-values ideally should lie along the diagonal line (with the majority of the p-values falling within the gray confidence band), indicating uniformity of the p-values in the tail of the distribution.
The lower left panel is a histogram of the estimated log-2 fold changes. The histogram ideally should be roughly symmetric and centered around zero.
Finally, the bottom right panel is a text box displaying (i) the number of false discoveries that sceptre has made on the negative control data and (ii) the mean estimated log-fold change.

Usage

plot_run_calibration_check(
  sceptre_object,
  point_size = 0.55,
  transparency = 0.8,
  return_indiv_plots = FALSE
)

Arguments

sceptre_object: a sceptre_object that has had run_calibration_check called on it
point_size: (optional; default 0.55) the size of the individual points in the plot
transparency: (optional; default 0.8) the transparency of the individual points in the plot
return_indiv_plots: (optional; default FALSE) if FALSE then a list of ggplot is returned; if TRUE then a single cowplot object is returned.

Value

a single cowplot object containing the combined panels (if return_indiv_plots is set to TRUE) or a list of the individual panels (if return_indiv_plots is set to FALSE)

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
)
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
  ) |>
  plot_run_calibration_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)