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Build limma-voom model with LOD imputation for failed proteins

Source: R/ContrastsLimma.R
build_model_limma_voom_impute.Rd

Combines vooma precision weights with LOD imputation for proteins that have entire missing groups (NA coefficients). Mirrors build_model_limma_impute but uses vooma weights.

Usage

build_model_limma_voom_impute(
  lfqdata,
  strategy,
  modelName = paste0(strategy$model_name, "Imputed"),
  lod = NULL,
  df_method = c("observed", "borrowed"),
  span = 0.5,
  plot = FALSE
)

Arguments

lfqdata

an LFQData object (aggregated to protein level)

strategy

output of strategy_limma

modelName

name of model (default: strategy name + "Imputed")

lod

numeric limit of detection; if NULL, auto-computed from data

df_method

how to set degrees of freedom for imputed proteins: "observed" (default) uses max(n_observed - p, 1); "borrowed" uses the median df from successful fits

span

lowess smoother span for vooma trend (default 0.5)

plot

logical; if TRUE, plot the mean-variance trend

Value

a ModelLimma object with a hybrid fit

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, ContrastsLimpaFacade, ContrastsLmerFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsRLMFacade, ContrastsROPECA, ContrastsROPECAFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, StrategyLM, StrategyLimma, StrategyLimpa, StrategyLmer, StrategyLogistf, StrategyRLM, build_contrast_analysis(), build_model(), build_model_glm_peptide(), build_model_glm_protein(), build_model_impute(), build_model_limma(), build_model_limma_impute(), build_model_limma_voom(), build_model_limpa(), build_model_logistf(), compute_borrowed_variance(), compute_borrowed_variance_limma(), compute_contrast(), compute_lmer_contrast(), contrasts_fisher_exact(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), impute_refit_singular(), 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(), moderated_p_deqms_long(), moderated_p_limma(), moderated_p_limma_long(), new_lm_imputed(), 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_limpa(), strategy_logistf(), summary_ROPECA_median_p.scaled()

Examples

istar <- sim_lfq_data_protein_config(Nprot = 50, weight_missing = 0.5)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
lfqdata$rename_response("transformedIntensity")

strat <- strategy_limma("transformedIntensity ~ group_")
mod <- build_model_limma_voom_impute(lfqdata, strat)
#> Warning: Partial NA coefficients for 2 probe(s)
#> completing cases
mod$get_coefficients()
#> # A tibble: 147 × 6
#>    protein_Id  factor      Estimate Std..Error t.value  Pr...t..
#>    <chr>       <chr>          <dbl>      <dbl>   <dbl>     <dbl>
#>  1 0EfVhX~7161 (Intercept)     20.6      0.528    39.0 7.04e-121
#>  2 0m5WN4~3543 (Intercept)     20.8      0.529    39.4 4.55e-122
#>  3 76k03k~9735 (Intercept)     20.2      0.466    43.2 1.40e-132
#>  4 7QuTub~5556 (Intercept)     22.8      0.528    43.2 2.09e-132
#>  5 7cbcrd~0495 (Intercept)     16.7      0.184    90.6 1.32e-223
#>  6 7soopj~3451 (Intercept)     26.3      0.475    55.5 3.74e-162
#>  7 9VUkAq~8655 (Intercept)     22.2      0.508    43.8 4.98e-134
#>  8 At886V~0359 (Intercept)     17.1      0.826    20.7 2.96e- 60
#>  9 BEJI92~5483 (Intercept)     15.9      0.827    19.2 1.95e- 54
#> 10 CGzoYe~1248 (Intercept)     18.2      0.585    31.2 2.78e- 97
#> # ℹ 137 more rows