plotQQ {benchdamic} | R Documentation |
Draw the average QQ-plots across the mock comparisons.
plotQQ(df_QQ, cols = NULL, zoom = c(0, 0.1))
df_QQ |
Coordinates to draw the QQ-plot to compare the mean observed p-value distribution across comparisons, with the theoretical uniform distribution. |
cols |
named vector of colors. |
zoom |
2-dimesional vector containing the starting and the
final coordinates (default: |
A ggplot object.
# Load some data data(ps_stool_16S) # Generate the patterns for 10 mock comparison for an experiment # (N = 1000 is suggested) mocks <- createMocks(nsamples = phyloseq::nsamples(ps_stool_16S), N = 10) head(mocks) # Add some normalization/scaling factors to the phyloseq object my_norm <- setNormalizations(fun = c("norm_edgeR", "norm_CSS"), method = c("TMM", "median")) ps_stool_16S <- runNormalizations(normalization_list = my_norm, object = ps_stool_16S) # Initialize some limma based methods my_limma <- set_limma(design = ~ group, coef = 2, norm = c("TMM", "CSSmedian")) # Run methods on mock datasets results <- runMocks(mocks = mocks, method_list = my_limma, object = ps_stool_16S) # Prepare results for Type I Error Control TIEC_summary <- createTIEC(results) # Plot the results plotFPR(df_FPR = TIEC_summary$df_FPR) plotQQ(df_QQ = TIEC_summary$df_QQ, zoom = c(0, 0.1)) plotKS(df_KS = TIEC_summary$df_KS)