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DEqMS-style count-dependent variance moderation

Source: R/ContrastsModeratedDEqMS.R
moderated_p_deqms.Rd

Applies count-dependent empirical Bayes variance shrinkage to a contrast result table. Proteins quantified from many peptides get less shrinkage; proteins from few peptides get more.

Usage

moderated_p_deqms(
  mm,
  count_col,
  df = "df",
  estimate = "diff",
  loess_span = 0.75,
  confint = 0.95
)

Arguments

mm

data.frame from one contrast group with columns: sigma, df, statistic, std.error, and the estimate column

count_col

name of column with peptide/PSM count per protein

df

name of the degrees of freedom column

estimate

name of the fold change column

loess_span

span parameter for LOESS fit (default 0.75)

confint

confidence level for intervals (default 0.95)

Value

data.frame with added moderated.* columns

See also

Other modelling: Contrasts, ContrastsFirth, ContrastsLimma, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsProDA, ContrastsROPECA, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, build_model(), build_model_limma(), build_model_logistf(), contrasts_fisher_exact(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), isSingular_lm(), linfct_all_possible_contrasts(), linfct_factors_contrasts(), linfct_from_model(), linfct_matrix_contrasts(), merge_contrasts_results(), model_analyse(), model_summary(), moderated_p_deqms_long(), 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_build_models_logistf(), sim_make_model_lm(), sim_make_model_lmer(), strategy_limma(), strategy_logistf(), summary_ROPECA_median_p.scaled()