RLM contrast analysis facade

RLM contrast analysis facade

RLM contrast analysis facade

Details

Encapsulates the pipeline: strategy_rlm -> build_model -> Contrasts -> ContrastsModerated.

Supports options(prolfqua.vectorize = TRUE) for faster contrast computation. See build_contrast_analysis for details.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, ContrastsLimpaFacade, ContrastsLmerFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, 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

Model object

contrast

ContrastsModerated object

.lfqdata

stored reference to input LFQData

.contrast_names

names of the requested contrasts

Methods

Public methods

• ContrastsRLMFacade$new()

• ContrastsRLMFacade$get_contrasts()

• ContrastsRLMFacade$get_missing()

• ContrastsRLMFacade$get_Plotter()

• ContrastsRLMFacade$to_wide()

• ContrastsRLMFacade$clone()

---

Method new()

initialize

Usage

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

Arguments

lfqdata

LFQData object

modelstr

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

contrasts

named character vector of contrasts

...

passed to strategy_rlm

---

Method get_contrasts()

get contrast results

Usage

ContrastsRLMFacade$get_contrasts(...)

Arguments

...

passed to ContrastsModerated$get_contrasts

---

Method get_missing()

get protein × contrast pairs that could not be estimated

Usage

ContrastsRLMFacade$get_missing()

---

Method get_Plotter()

get ContrastsPlotter

Usage

ContrastsRLMFacade$get_Plotter(...)

Arguments

...

passed to ContrastsModerated$get_Plotter

---

Method to_wide()

convert results to wide format

Usage

ContrastsRLMFacade$to_wide(...)

Arguments

...

passed to ContrastsModerated$to_wide

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsRLMFacade$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 <- ContrastsRLMFacade$new(lfqdata, "~ group_", contrasts)
head(fa$get_contrasts())
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
#> # A tibble: 6 × 14
#>   facade modelName   protein_Id contrast   diff std.error avgAbd statistic    df
#>   <chr>  <chr>       <chr>      <chr>     <dbl>     <dbl>  <dbl>     <dbl> <dbl>
#> 1 rlm    WaldTest_m… 0EfVhX~00… A_vs_Ct… -2.44      0.680   21.0    -3.51   12.9
#> 2 rlm    WaldTest_m… 7cbcrd~57… A_vs_Ct…  2.79      0.434   20.6     4.89   10.1
#> 3 rlm    WaldTest_m… 9VUkAq~47… A_vs_Ct…  1.74      0.352   20.4     4.06   10.7
#> 4 rlm    WaldTest_m… BEJI92~52… A_vs_Ct…  0.951     0.472   20.7     2.04   12.6
#> 5 rlm    WaldTest_m… CGzoYe~21… A_vs_Ct… -0.579     0.914   30.7    -0.728  14.9
#> 6 rlm    WaldTest_m… Fl4JiV~86… A_vs_Ct… -0.174     0.715   21.2    -0.239  13.8
#> # ℹ 5 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> #   sigma <dbl>, FDR <dbl>
fa$to_wide()
#> # A tibble: 9 × 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~0087         -2.44           0.00389        0.0117               -3.51 
#> 2 7cbcrd~5725          2.79           0.000610       0.00549               4.89 
#> 3 9VUkAq~4703          1.74           0.00197        0.00887               4.06 
#> 4 BEJI92~5282          0.951          0.0626         0.141                 2.04 
#> 5 CGzoYe~2147         -0.579          0.478          0.610                -0.728
#> 6 Fl4JiV~8625         -0.174          0.815          0.815                -0.239
#> 7 HvIpHG~9079         -0.585          0.399          0.610                -0.870
#> 8 JcKVfU~9653          0.328          0.542          0.610                 0.627
#> 9 SGIVBl~5782         -0.494          0.482          0.610                -0.723