check_P | R Documentation |
A simple test to check if the reported p-values in a GWAS results file match the other statistics. This function calculates an expected p-value (from the effect size and standard error) and then correlates it with the actual, reported p-value.
check_P(dataset, HQ_subset, plot_correlation = FALSE, plot_if_threshold = FALSE, threshold_r = 0.99, save_name = "dataset", save_dir = getwd(), header_translations, use_log = FALSE, dataN = nrow(dataset), ...)
dataset |
table with at least three columns: p-value, effect size and standard error. |
HQ_subset |
an optional logical or numeric vector
indicating the rows in |
plot_correlation |
logical; should a scatterplot of
the reported vs. calculated p-values be made? If |
plot_if_threshold |
logical; if |
threshold_r |
numeric; the correlation threshold for the scatterplot. |
save_name |
character string; the filename, without extension, for the scatterplot. |
save_dir |
character string; the directory where the output files are saved. Note that R uses forward slash (/) where Windows uses backslash (\). |
header_translations |
translation table for column names
See |
use_log, dataN |
arguments used by |
... |
arguments passed to |
check_P
calculates the expected p-value by taking the
chi-square (1 degree of freedom) of the effect size divided by
the standard error squared.
In a typical GWAS dataset, the expected and observed p-values should correlate perfectly. If this isn't the case, the problem either lies in a misidentified column, or the wrong values were used when generating the dataset.
The correlation between expected and reported p-values.
data("gwa_sample") selected_SNPs <- HQ_filter(data = gwa_sample, FRQ_val = 0.05, cal_val = 0.95, filter_NA = FALSE) # To calculate a correlation between predicted and actual p-values: check_P(gwa_sample, HQ_subset = selected_SNPs, plot_correlation = FALSE) # To plot the correlation: ## Not run: check_P(gwa_sample, HQ_subset = selected_SNPs, plot_correlation = TRUE, plot_if_threshold = FALSE, save_name = "sample") ## End(Not run)
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