LM contrast analysis facade

LM contrast analysis facade

LM contrast analysis facade

Details

Encapsulates the pipeline: strategy_lm -> 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, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, 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

Model object

contrast

ContrastsModerated object

.lfqdata

stored reference to input LFQData

.contrast_names

names of the requested contrasts

Methods

Public methods

• ContrastsLMFacade$new()

• ContrastsLMFacade$get_contrasts()

• ContrastsLMFacade$get_missing()

• ContrastsLMFacade$get_Plotter()

• ContrastsLMFacade$to_wide()

• ContrastsLMFacade$clone()

---

Method new()

initialize

Usage

ContrastsLMFacade$new(
  lfqdata,
  modelstr,
  contrasts,
  weights = lfqdata$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_lm

---

Method get_contrasts()

get contrast results

Usage

ContrastsLMFacade$get_contrasts(...)

Arguments

...

passed to ContrastsModerated$get_contrasts

---

Method get_missing()

get protein × contrast pairs that could not be estimated

Usage

ContrastsLMFacade$get_missing()

---

Method get_Plotter()

get ContrastsPlotter

Usage

ContrastsLMFacade$get_Plotter(...)

Arguments

...

passed to ContrastsModerated$get_Plotter

---

Method to_wide()

convert results to wide format

Usage

ContrastsLMFacade$to_wide(...)

Arguments

...

passed to ContrastsModerated$to_wide

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsLMFacade$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 <- ContrastsLMFacade$new(lfqdata, "~ group_", contrasts)
head(fa$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 lm     WaldTest_… 0EfVhX~00… A_vs_Ct… -2.62       0.660   21.1   -4.12   11.5 
#> 2 lm     WaldTest_… 7cbcrd~57… A_vs_Ct…  2.80       0.417   20.7    4.29    9.55
#> 3 lm     WaldTest_… 9VUkAq~47… A_vs_Ct…  1.67       0.740   20.3    1.87   10.5 
#> 4 lm     WaldTest_… BEJI92~52… A_vs_Ct…  0.424      0.960   21.0    0.476  11.5 
#> 5 lm     WaldTest_… CGzoYe~21… A_vs_Ct… -0.598      0.750   30.8   -0.744  12.5 
#> 6 lm     WaldTest_… Fl4JiV~86… A_vs_Ct… -0.0494     0.603   21.3   -0.0862 11.5 
#> # 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        -2.62             0.00154       0.00789             -4.12  
#> 2 7cbcrd~5725         2.80             0.00175       0.00789              4.29  
#> 3 9VUkAq~4703         1.67             0.0893        0.268                1.87  
#> 4 BEJI92~5282         0.424            0.643         0.723                0.476 
#> 5 CGzoYe~2147        -0.598            0.470         0.605               -0.744 
#> 6 Fl4JiV~8625        -0.0494           0.933         0.933               -0.0862
#> 7 HvIpHG~9079        -0.809            0.287         0.605               -1.12  
#> 8 JcKVfU~9653         0.642            0.411         0.605                0.851 
#> 9 SGIVBl~5782        -0.494            0.441         0.605               -0.798