Limma contrast analysis with LOD imputation facade

Limma contrast analysis with LOD imputation facade

Limma contrast analysis with LOD imputation facade

Value

An R6 class generator.

Details

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

Proteins whose limma fit produces NA coefficients (typically from entire missing groups) are recovered by imputing missing values with the limit of detection (LOD) and refitting. The variance is borrowed from successful fits and degrees of freedom are corrected so that inference is not artificially precise from the constant imputation.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFacadeBase, ContrastsFirth, ContrastsFirthFacade, ContrastsFirthNestedFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, ContrastsLimpaFacade, ContrastsLimpaNestedFacade, ContrastsLmerNestedFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsRLMFacade, ContrastsROPECA, ContrastsROPECANestedFacade, ContrastsRfitFacade, ContrastsRfitImputeFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, StrategyLM, StrategyLimma, StrategyLimpa, StrategyLmer, StrategyLogistf, StrategyRLM, StrategyRfit, 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(), df.residual.rfit_prolfqua(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), impute_refit_singular(), is_singular_lm(), linfct_all_possible_contrasts(), linfct_factors_contrasts(), linfct_from_model(), linfct_matrix_contrasts(), list_facades(), lookup_facade(), merge_contrasts_results(), model_analyse(), model_summary(), moderated_p_deqms(), moderated_p_deqms_long(), moderated_p_limma(), moderated_p_limma_long(), new_imputed_model(), pivot_model_contrasts_to_wide(), plot_lmer_peptide_predictions(), register_facade(), sigma.rfit_prolfqua(), 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(), unregister_facade(), vcov.rfit_prolfqua()

Super classes

prolfqua::ContrastsInterface -> prolfqua::ContrastsFacadeBase -> ContrastsLimmaImputeFacade

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

• ContrastsLimmaImputeFacade$new()

• ContrastsLimmaImputeFacade$clone()

Inherited methods

• prolfqua::ContrastsInterface$column_description()
• prolfqua::ContrastsInterface$contrast_summary_table()
• prolfqua::ContrastsInterface$extra_artifacts()
• prolfqua::ContrastsInterface$filter_significant()
• prolfqua::ContrastsInterface$get_config()
• prolfqua::ContrastsInterface$get_contrast_sides()
• prolfqua::ContrastsInterface$get_ora()
• prolfqua::ContrastsInterface$get_rank()
• prolfqua::ContrastsFacadeBase$get_Plotter()
• prolfqua::ContrastsFacadeBase$get_contrasts()
• prolfqua::ContrastsFacadeBase$get_missing()
• prolfqua::ContrastsFacadeBase$to_wide()

---

Method new()

initialize

Usage

ContrastsLimmaImputeFacade$new(
  lfqdata,
  modelstr,
  contrasts,
  lod = NULL,
  df_method = c("observed", "borrowed"),
  weights = lfqdata$nr_children_col(),
  ...
)

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$nr_children_col()). Pass NULL for unweighted.

...

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

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsLimmaImputeFacade$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 <- ContrastsLimmaImputeFacade$new(lfqdata, "~ group_", contrasts)
#> Warning: Partial NA coefficients for 4 probe(s)
head(fa$get_contrasts())
#> # A tibble: 6 × 16
#>   modelName    estimate_type protein_Id  contrast     diff   FDR
#>   <chr>        <chr>         <chr>       <chr>       <dbl> <dbl>
#> 1 limma_impute observed      0EfVhX~2956 A_vs_Ctrl  1.24   0.427
#> 2 limma_impute observed      0m5WN4~6730 A_vs_Ctrl -0.0361 0.971
#> 3 limma_impute observed      7QuTub~6175 A_vs_Ctrl  0.909  0.773
#> 4 limma_impute observed      7cbcrd~2687 A_vs_Ctrl  0.612  0.855
#> 5 limma_impute observed      9VUkAq~3402 A_vs_Ctrl  0.768  0.855
#> 6 limma_impute observed      At886V~7759 A_vs_Ctrl -1.86   0.212
#> # ℹ 10 more variables: std.error.unmoderated <dbl>, df.unmoderated <dbl>,
#> #   std.error <dbl>, statistic <dbl>, p.value <dbl>, 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             0.0853          0.427              1.86  
#>  2 0m5WN4~67…        -0.0361           0.971           0.971             -0.0374
#>  3 7QuTub~61…         0.909            0.352           0.773              1.00  
#>  4 7cbcrd~26…         0.612            0.570           0.855              0.588 
#>  5 9VUkAq~34…         0.768            0.519           0.855              0.664 
#>  6 At886V~77…        -1.86             0.0258          0.212             -2.50  
#>  7 BEJI92~27…        -1.30             0.182           0.545             -1.41  
#>  8 CGzoYe~08…         0.196            0.850           0.971              0.194 
#>  9 CtOJ9t~91…        -0.0717           0.929           0.971             -0.0912
#> 10 DoWup2~28…        -1.82             0.00858         0.129             -3.07  
#> # ℹ 20 more rows