Limpa contrast analysis facade

Limpa contrast analysis facade

Limpa contrast analysis facade

Value

An R6 class generator.

Details

Encapsulates the pipeline: strategy_limpa -> build_model_limpa -> ContrastsLimma.

Requires protein-level LFQData produced by AggregateLimpa, which provides the standard error (config$opt_se) and observation count (config$nr_children) columns needed for limpa's vooma precision weighting and imputation-aware DF correction.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, 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(), is_singular_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_to_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 (from build_model_limpa)

contrast

ContrastsLimma object

.lfqdata

stored reference to input LFQData

.contrast_names

names of the requested contrasts

Methods

Public methods

• ContrastsLimpaFacade$new()

• ContrastsLimpaFacade$get_contrasts()

• ContrastsLimpaFacade$get_missing()

• ContrastsLimpaFacade$get_Plotter()

• ContrastsLimpaFacade$to_wide()

• ContrastsLimpaFacade$clone()

---

Method new()

initialize

Usage

ContrastsLimpaFacade$new(
  lfqdata,
  modelstr,
  contrasts,
  plot = FALSE,
  span = NULL,
  ...
)

Arguments

lfqdata

LFQData from AggregateLimpa (must have config$opt_se set)

modelstr

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

contrasts

named character vector of contrasts

plot

logical; if TRUE, plot the vooma mean-variance trend

span

lowess smoother span (NULL = auto)

...

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

---

Method get_contrasts()

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

Usage

ContrastsLimpaFacade$get_contrasts(...)

Arguments

...

passed to ContrastsLimma$get_contrasts

---

Method get_missing()

get protein x contrast pairs that could not be estimated

Usage

ContrastsLimpaFacade$get_missing()

---

Method get_Plotter()

get ContrastsPlotter

Usage

ContrastsLimpaFacade$get_Plotter(...)

Arguments

...

passed to ContrastsLimma$get_Plotter

---

Method to_wide()

convert results to wide format

Usage

ContrastsLimpaFacade$to_wide(...)

Arguments

...

passed to ContrastsLimma$to_wide

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsLimpaFacade$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- prolfqua::sim_lfq_data_peptide_config()
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
lfqdata <- lfqdata$get_Transformer()$log2()$lfq
#> Column added : log2_abundance
agg <- AggregateLimpa$new(lfqdata, "protein")
lfq_agg <- agg$aggregate()
contrasts <- c("A_vs_Ctrl" = "group_A - group_Ctrl")
fa <- ContrastsLimpaFacade$new(lfq_agg, "~ group_", contrasts)
head(fa$get_contrasts())
#> # A tibble: 6 × 14
#>   facade modelName protein_Id  contrast     diff       FDR std.error statistic
#>   <chr>  <chr>     <chr>       <chr>       <dbl>     <dbl>     <dbl>     <dbl>
#> 1 limpa  limpa     0EfVhX~0087 A_vs_Ctrl -0.0244 0.407        0.0283    -0.862
#> 2 limpa  limpa     7cbcrd~5725 A_vs_Ctrl  0.725  0.00458      0.185      3.91 
#> 3 limpa  limpa     9VUkAq~4703 A_vs_Ctrl -0.572  0.000276     0.0991    -5.78 
#> 4 limpa  limpa     BEJI92~5282 A_vs_Ctrl  0.236  0.0138       0.0739     3.19 
#> 5 limpa  limpa     CGzoYe~2147 A_vs_Ctrl -0.296  0.146        0.175     -1.70 
#> 6 limpa  limpa     DoWup2~5896 A_vs_Ctrl  0.282  0.0000889    0.0417     6.77 
#> # ℹ 6 more variables: p.value <dbl>, sigma <dbl>, df <dbl>, conf.low <dbl>,
#> #   conf.high <dbl>, avgAbd <dbl>