Draw the nominal false discovery rates for the 0.01, 0.05, and 0.1 levels.
plotFDR(df_FDR, cols = NULL)
named vector of colors.
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", "CSS")) 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", "CSS")) # 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$df_FPR) plotFDR(df_FDR = TIEC_summary$df_FDR) plotQQ(df_QQ = TIEC_summary$df_QQ, zoom = c(0, 0.1)) plotKS(df_KS = TIEC_summary$df_KS) plotLogP(df_QQ = TIEC_summary$df_QQ)
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