Limma-voom contrast analysis with LOD imputation facade

Limma-voom contrast analysis with LOD imputation facade

Limma-voom contrast analysis with LOD imputation facade

Details

Encapsulates the pipeline: strategy_limma -> build_model_limma_voom_impute -> ContrastsLimma.

Combines vooma precision weights with LOD imputation for proteins whose fit produces NA coefficients (typically from entire missing groups).

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, 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_limma_voom_impute(), 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()

Public fields

model

ModelLimma object (with imputed proteins)

contrast

ContrastsLimma object

.lfqdata

stored reference to input LFQData

.contrast_names

names of the requested contrasts

Methods

Public methods

• ContrastsLimmaVoomImputeFacade$new()

• ContrastsLimmaVoomImputeFacade$get_contrasts()

• ContrastsLimmaVoomImputeFacade$get_missing()

• ContrastsLimmaVoomImputeFacade$get_Plotter()

• ContrastsLimmaVoomImputeFacade$to_wide()

• ContrastsLimmaVoomImputeFacade$clone()

---

Method new()

initialize

Usage

ContrastsLimmaVoomImputeFacade$new(
  lfqdata,
  modelstr,
  contrasts,
  lod = NULL,
  df_method = c("observed", "borrowed"),
  weights = lfqdata$config$nr_children,
  span = 0.5,
  plot = FALSE,
  ...
)

Arguments

lfqdata

LFQData object (aggregated to protein level)

modelstr

model formula string (e.g. "~ group_")

contrasts

named character vector of contrasts

lod

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

df_method

"observed" uses max(n_observed - p, 1); "borrowed" uses median df from successful fits

weights

column name for per-observation weights (default: lfqdata$config$nr_children). Pass NULL for unweighted.

span

lowess smoother span for vooma trend (default 0.5)

plot

logical; if TRUE, plot the mean-variance trend

...

passed to strategy_limma (e.g. trend, robust)

---

Method get_contrasts()

get contrast results (rows with NA diff are filtered out)

Usage

ContrastsLimmaVoomImputeFacade$get_contrasts(...)

Arguments

...

passed to ContrastsLimma$get_contrasts

---

Method get_missing()

get protein x contrast pairs that could not be estimated

Usage

ContrastsLimmaVoomImputeFacade$get_missing()

---

Method get_Plotter()

get ContrastsPlotter

Usage

ContrastsLimmaVoomImputeFacade$get_Plotter(...)

Arguments

...

passed to ContrastsLimma$get_Plotter

---

Method to_wide()

convert results to wide format

Usage

ContrastsLimmaVoomImputeFacade$to_wide(...)

Arguments

...

passed to ContrastsLimma$to_wide

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsLimmaVoomImputeFacade$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- sim_lfq_data_protein_config(Nprot = 30, 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")
contrasts <- c("A_vs_Ctrl" = "group_A - group_Ctrl")
fa <- ContrastsLimmaVoomImputeFacade$new(lfqdata, "~ group_", contrasts)
#> Warning: Partial NA coefficients for 4 probe(s)
#> completing cases
head(fa$get_contrasts())
#> # A tibble: 6 × 14
#>   facade modelName protein_Id contrast    diff   FDR std.error statistic p.value
#>   <chr>  <chr>     <chr>      <chr>      <dbl> <dbl>     <dbl>     <dbl>   <dbl>
#> 1 limma… limma     0EfVhX~29… A_vs_Ct…  1.24   0.325     0.617    2.00    0.0650
#> 2 limma… limma     0m5WN4~67… A_vs_Ct… -0.0361 0.973     1.03    -0.0352  0.973 
#> 3 limma… limma     7QuTub~61… A_vs_Ct…  0.909  0.694     0.813    1.12    0.301 
#> 4 limma… limma     7cbcrd~26… A_vs_Ct…  0.612  0.855     0.968    0.633   0.541 
#> 5 limma… limma     9VUkAq~34… A_vs_Ct…  0.768  0.855     1.15     0.671   0.514 
#> 6 limma… limma     At886V~77… A_vs_Ct… -1.86   0.240     0.822   -2.26    0.0400
#> # ℹ 5 more variables: sigma <dbl>, df <dbl>, conf.low <dbl>, conf.high <dbl>,
#> #   avgAbd <dbl>
fa$to_wide()
#> # A tibble: 30 × 5
#>    protein_Id diff.A_vs_Ctrl p.value.A_vs_Ctrl FDR.A_vs_Ctrl statistic.A_vs_Ctrl
#>    <chr>               <dbl>             <dbl>         <dbl>               <dbl>
#>  1 0EfVhX~29…         1.24             0.0650         0.325               2.00  
#>  2 0m5WN4~67…        -0.0361           0.973          0.973              -0.0352
#>  3 7QuTub~61…         0.909            0.301          0.694               1.12  
#>  4 7cbcrd~26…         0.612            0.541          0.855               0.633 
#>  5 9VUkAq~34…         0.768            0.514          0.855               0.671 
#>  6 At886V~77…        -1.86             0.0400         0.240              -2.26  
#>  7 BEJI92~27…        -1.30             0.154          0.462              -1.51  
#>  8 CGzoYe~08…         0.196            0.849          0.951               0.194 
#>  9 CtOJ9t~91…        -0.0717           0.931          0.973              -0.0882
#> 10 DoWup2~28…        -1.82             0.00796        0.0865             -3.09  
#> # ℹ 20 more rows