compute protein level fold changes and p.values (using beta distribution) takes p-value of the scaled p-value

compute protein level fold changes and p.values (using beta distribution) takes p-value of the scaled p-value

summary_ROPECA_median_p.scaled(
  contrasts_data,
  contrast = "contrast",
  subject_Id = "protein_Id",
  estimate = "diff",
  statistic = "statistic",
  p.value = "moderated.p.value",
  max.n = 10
)

Arguments

contrasts_data

data frame

contrast

name of column with contrast identifier

subject_Id

name of column with typically protein Id

estimate

name of column with effect size estimate

statistic

statistic name of column with statistic (typically t-statistics)

p.value

name of column with moderated.p.value

max.n

used to limit the number of peptides in probablity computation.

Value

data.frame with columns

See also

Other modelling: Contrasts, ContrastsMissing, ContrastsModerated, ContrastsPlotter, ContrastsProDA, ContrastsROPECA, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, build_model(), contrasts_fisher_exact(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), isSingular_lm(), linfct_all_possible_contrasts(), linfct_factors_contrasts(), linfct_from_model(), linfct_matrix_contrasts(), merge_contrasts_results(), model_analyse(), model_summary(), moderated_p_limma(), moderated_p_limma_long(), my_contest(), my_contrast(), my_contrast_V1(), my_contrast_V2(), my_glht(), pivot_model_contrasts_2_Wide(), plot_lmer_peptide_predictions(), sim_build_models_lm(), sim_build_models_lmer(), sim_make_model_lm(), sim_make_model_lmer(), strategy_lmer()

Examples

set.seed(10)
nrPep <- 10000
nrProtein <- 800
p.value <- runif(nrPep)
estimate <- rnorm(nrPep)
avgAbd <- runif(nrPep)
protein_Id <- sample(1:800, size = nrPep,
  replace = TRUE, prob = dexp(seq(0,5,length = 800)))

plot(table(table(protein_Id)))


testdata <- data.frame(contrast = "contrast1",
  protein_Id = protein_Id,
  estimate = estimate,
  pseudo_estimate = estimate,
  p.value = p.value,
  avgAbd = avgAbd )

xx30 <- summary_ROPECA_median_p.scaled(testdata,
                                    subject_Id = "protein_Id",
                                    estimate = "estimate",
                                    p.value = "p.value",
                                    max.n = 30)

xx2 <- summary_ROPECA_median_p.scaled(testdata,
                                    subject_Id = "protein_Id",
                                    estimate = "estimate",
                                    p.value = "p.value",
                                    max.n = 1)

testthat::expect_equal(mad(xx2$estimate, na.rm = TRUE),0.384409, tolerance = 1e-4)
testthat::expect_equal(median(xx2$estimate), -0.006874857, tolerance = 1e-4)
testthat::expect_equal(xx2$beta.based.significance[1],0.819, tolerance = 1e-3)
testthat::expect_equal(xx2$beta.based.significance[2],0.9234362,tolerance = 1e-3)

# Uniform distribution
hist(testdata$p.value)

hist(xx30$median.p.scaled, breaks = 20)

hist(xx2$median.p.scaled, breaks = 20)

# shows that beta.based.significance has NO uniform distribution
# although H0 is true for all cases.

hist(xx30$beta.based.significance, breaks = 20)

hist(xx2$beta.based.significance, breaks = 20)


hist(xx2$median.p.value, breaks = 20)

hist(xx2$beta.based.significance, breaks = 20)

hist(estimate)