View source: R/plotting_functions.R
plot_run_power_check | R Documentation |
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.
plot_run_power_check(
sceptre_object,
point_size = 1,
transparency = 0.8,
clip_to = 1e-20
)
sceptre_object |
a |
point_size |
(optional; default |
transparency |
(optional; default |
clip_to |
(optional; default |
a single ggplot2
plot
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
)
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(
parallel = TRUE,
n_processors = 2,
n_calibration_pairs = 500,
calibration_group_size = 2
) |>
run_power_check() |>
plot_run_power_check()
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