DEqMS contrast analysis with vooma weights facade

DEqMS contrast analysis with vooma weights facade

DEqMS contrast analysis with vooma weights facade

Value

An R6 class generator.

Details

Combines vooma precision weights (mean-variance trend) with DEqMS count-dependent variance moderation. Vooma runs without external weights so it captures only the mean-variance relationship; the peptide count enters solely through DEqMS moderation, avoiding double-counting.

Pipeline: strategy_limma (weights = NULL) -> build_model_limma_voom -> ContrastsLimma (eBayes = FALSE) -> ContrastsModeratedDEqMS.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsFacadeBase, ContrastsFirth, ContrastsFirthFacade, ContrastsFirthNestedFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, 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 -> ContrastsDEqMSVoomFacade

Public fields

model

ModelLimma object

contrast

ContrastsModeratedDEqMS object

.lfqdata

stored reference to input LFQData

.contrast_names

names of the requested contrasts

Methods

Public methods

• ContrastsDEqMSVoomFacade$new()

• ContrastsDEqMSVoomFacade$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

ContrastsDEqMSVoomFacade$new(
  lfqdata,
  modelstr,
  contrasts,
  span = 0.5,
  plot = FALSE,
  ...
)

Arguments

lfqdata

LFQData object

modelstr

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

contrasts

named character vector of contrasts

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 clone()

The objects of this class are cloneable with this method.

Usage

ContrastsDEqMSVoomFacade$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- sim_lfq_data_protein_config(Nprot = 50)
#> 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 <- ContrastsDEqMSVoomFacade$new(lfqdata, "~ group_", contrasts)
#> Warning: Partial NA coefficients for 1 probe(s)
head(fa$get_contrasts())
#> Warning: NaNs produced
#> Warning: moderated_p_deqms_long: condition messages in 1/1 groups. contrast=A_vs_Ctrl (pseudoinverse used at 1; neighborhood radius 1; reciprocal condition number  2.362e-17; 'newdata' had 1 row but variables found have 49 rows; number of items to replace is not a multiple of replacement length; NaNs produced)
#> # A tibble: 6 × 16
#>   modelName  estimate_type contrast  protein_Id    diff std.error.unmoderated
#>   <chr>      <chr>         <chr>     <chr>        <dbl>                 <dbl>
#> 1 deqms_voom observed      A_vs_Ctrl 0EfVhX~7161  3.00                  0.897
#> 2 deqms_voom observed      A_vs_Ctrl 0m5WN4~3543  0.222                 0.906
#> 3 deqms_voom observed      A_vs_Ctrl 76k03k~9735  0.509                 0.461
#> 4 deqms_voom observed      A_vs_Ctrl 7QuTub~5556 -1.22                  0.874
#> 5 deqms_voom observed      A_vs_Ctrl 7cbcrd~0495  1.38                  0.742
#> 6 deqms_voom observed      A_vs_Ctrl 7soopj~3451  0.822                 0.618
#> # ℹ 10 more variables: df.unmoderated <dbl>, avgAbd <dbl>, std.error <dbl>,
#> #   statistic <dbl>, df <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> #   sigma <dbl>, FDR <dbl>