get_result() returns a data frame containing the result of a calibration check, power check, or discovery analysis. We pass as arguments sceptre_object and analysis, where the latter is a string indicating the function whose results we are querying. The output is a data frame, the rows of which correspond to target-response pairs, and the columns of which are as follows: response_id, grna_target, n_nonzero_trt, n_nonzero_cntrl, pass_qc (a TRUE/FALSE value indicating whether the pair passes pairwise QC), p_value, log_2_fold_change, and significant (a TRUE/FALSE value indicating whether the pair is called as significant). The p-value contained within the p_value column is a raw (i.e., non-multiplicity-adjusted) p-value. See Section 8 of the introductory chapter in the manual for more information about this function.
Arguments
- sceptre_object
a
sceptre_object- analysis
a string indicating the name of the analysis whose results we are querying, one of
"run_calibration_check","run_power_check", or"run_discovery_analysis".
Note
If output_amount is set to 2 or 3 in run_calibration_check(), run_power_check(), or run_discovery_analysis(), then the result data frame contains additional columns; see Chapter 6 in the manual for more information.
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
)
positive_control_pairs <- construct_positive_control_pairs(sceptre_object)
pc_result <- sceptre_object |>
set_analysis_parameters(
side = "left",
resampling_mechanism = "permutations",
positive_control_pairs = positive_control_pairs
) |>
assign_grnas(method = "thresholding") |>
run_qc() |>
run_power_check() |>
get_result("run_power_check")
#> Note: If you are on a Mac laptop or desktop, consider setting `parallel = TRUE` to improve speed. Otherwise, keep `parallel = FALSE`.
#>
#> Warning: The calibration check (`run_calibration_check()`) should be run before the power check.
#>
#> Generating permutation resamples. ✓
#> Analyzing pairs containing response ENSG00000104131 (1 of 10)
#> Analyzing pairs containing response ENSG00000147669 (5 of 10)
#> Analyzing pairs containing response ENSG00000176890 (10 of 10)