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Limma contrast analysis facade

Source: R/ContrastsFacades.R
ContrastsLimmaFacade.Rd

Limma contrast analysis facade

Limma contrast analysis facade

Details

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

Supports options(prolfqua.vectorize = TRUE) for faster linfct_matrix_contrasts evaluation. See build_contrast_analysis for details.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, 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

contrast

ContrastsLimma object

.lfqdata

stored reference to input LFQData

.contrast_names

names of the requested contrasts

Methods

Public methods

Method new()

initialize

Usage

ContrastsLimmaFacade$new(
  lfqdata,
  modelstr,
  contrasts,
  weights = lfqdata$config$nr_children,
  ...
)

Arguments

lfqdata

LFQData object

modelstr

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

contrasts

named character vector of contrasts

weights

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

...

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

Method get_contrasts()

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

Usage

ContrastsLimmaFacade$get_contrasts(...)

Arguments

...

passed to ContrastsLimma$get_contrasts

Method get_missing()

get protein × contrast pairs that could not be estimated

Usage

ContrastsLimmaFacade$get_missing()

Method get_Plotter()

get ContrastsPlotter

Usage

ContrastsLimmaFacade$get_Plotter(...)

Arguments

...

passed to ContrastsLimma$get_Plotter

Method to_wide()

convert results to wide format

Usage

ContrastsLimmaFacade$to_wide(...)

Arguments

...

passed to ContrastsLimma$to_wide

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsLimmaFacade$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- sim_lfq_data_protein_config()
#> 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 <- ContrastsLimmaFacade$new(lfqdata, "~ group_", contrasts)
#> Warning: Partial NA coefficients for 1 probe(s)
head(fa$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 limma  limma     0EfVhX~0087 A_vs_Ctrl -2.62   0.00694     0.625   -4.19  
#> 2 limma  limma     7cbcrd~5725 A_vs_Ctrl  2.80   0.00694     0.585    4.78  
#> 3 limma  limma     9VUkAq~4703 A_vs_Ctrl  1.67   0.219       0.831    2.00  
#> 4 limma  limma     BEJI92~5282 A_vs_Ctrl  0.424  0.721       0.884    0.479 
#> 5 limma  limma     CGzoYe~2147 A_vs_Ctrl -0.598  0.583       0.771   -0.775 
#> 6 limma  limma     Fl4JiV~8625 A_vs_Ctrl -0.0494 0.932       0.566   -0.0873
#> # ℹ 6 more variables: p.value <dbl>, sigma <dbl>, df <dbl>, conf.low <dbl>,
#> #   conf.high <dbl>, avgAbd <dbl>
fa$to_wide()
#> # A tibble: 10 × 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~00…        -2.62             0.00154       0.00694             -4.19  
#>  2 7cbcrd~57…         2.80             0.00102       0.00694              4.78  
#>  3 9VUkAq~47…         1.67             0.0731        0.219                2.00  
#>  4 BEJI92~52…         0.424            0.641         0.721                0.479 
#>  5 CGzoYe~21…        -0.598            0.453         0.583               -0.775 
#>  6 DoWup2~58…        NA               NA            NA                   NA     
#>  7 Fl4JiV~86…        -0.0494           0.932         0.932               -0.0873
#>  8 HvIpHG~90…        -0.809            0.277         0.583               -1.14  
#>  9 JcKVfU~96…         0.642            0.418         0.583                0.839 
#> 10 SGIVBl~57…        -0.494            0.445         0.583               -0.792