Hurdle contrast facade for nested input

Hurdle contrast facade for nested input

Hurdle contrast facade for nested input

Value

An R6 class generator.

Details

Takes nested (peptide-level) LFQData and builds both hurdle components internally: median polish to protein level and lm_impute on the protein intensities, binomial_firth_nested on the peptide detection. The two are combined with ContrastsHurdleFacade. Use ContrastsHurdleFacade directly to pair other intensity or detection facades.

get_missing() reports against the median-polish protein data. The two fitted models are facade$contrast$intensity$model and facade$contrast$detection$model.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFacadeBase, ContrastsFirth, ContrastsFirthFacade, ContrastsFirthNestedFacade, ContrastsHurdleFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, ContrastsLimpaFacade, ContrastsLimpaNestedFacade, ContrastsLmerNestedFacade, 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(), df.residual.rfit_prolfqua(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), impute_from_model(), impute_refit_singular(), 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(), new_imputed_model(), pivot_model_contrasts_to_wide(), 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(), vcov.rfit_prolfqua()

Super classes

prolfqua::ContrastsInterface -> prolfqua::ContrastsFacadeBase -> ContrastsHurdleNestedFacade

Methods

Public methods

• ContrastsHurdleNestedFacade$new()

• ContrastsHurdleNestedFacade$clone()

Inherited methods

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

---

Method new()

initialize

Usage

ContrastsHurdleNestedFacade$new(lfqdata, modelstr, contrasts, ...)

Arguments

lfqdata

nested LFQData (peptide-level, log2-transformed)

modelstr

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

contrasts

named character vector of contrasts

...

ignored; accepted for dispatch compatibility with other facades

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsHurdleNestedFacade$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- sim_lfq_data_peptide_config(Nprot = 20, weight_missing = 0.5, seed = 3)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfq_peptide <- LFQData$new(istar$data, istar$config)
lfq_peptide <- lfq_peptide$get_Transformer()$log2()$lfq
#> Column added : log2_abundance
contrasts <- c("A_vs_Ctrl" = "group_A - group_Ctrl")
facade <- build_contrast_analysis(lfq_peptide, "~ group_", contrasts, method = "hurdle_nested")
#> starting aggregation
#> Warning: medpolish() did not converge in 10 iterations
#> completing cases
#> completing cases
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
head(facade$get_contrasts())
#> completing cases
#> # A tibble: 6 × 24
#>   modelName     estimate_type protein_Id  contrast     diff avgAbd
#>   <chr>         <chr>         <chr>       <chr>       <dbl>  <dbl>
#> 1 hurdle_nested observed      0GRprF~7339 A_vs_Ctrl -0.231    4.03
#> 2 hurdle_nested observed      4JK499~3111 A_vs_Ctrl  0.466    3.90
#> 3 hurdle_nested observed      7IZdVV~6818 A_vs_Ctrl  0.122    4.62
#> 4 hurdle_nested observed      AZPG26~9461 A_vs_Ctrl -0.0544   4.67
#> 5 hurdle_nested observed      AoNKbb~3497 A_vs_Ctrl  0.0672   4.52
#> 6 hurdle_nested observed      CibL2O~2149 A_vs_Ctrl -0.328    4.04
#> # ℹ 18 more variables: std.error.unmoderated <dbl>, df.unmoderated <int>,
#> #   std.error <dbl>, statistic <dbl>, df <dbl>, conf.low <dbl>,
#> #   conf.high <dbl>, sigma <dbl>, p.value <dbl>, FDR <dbl>,
#> #   p.value.intensity <dbl>, FDR.intensity <dbl>, diff.detection <dbl>,
#> #   p.value.detection <dbl>, FDR.detection <dbl>, chisq.hurdle <dbl>,
#> #   df.hurdle <dbl>, hurdle_components <chr>