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LM contrast analysis facade

Source: R/ContrastsFacades.R
ContrastsLMFacade.Rd

LM contrast analysis facade

LM contrast analysis facade

Value

An R6 class generator.

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, ContrastsFacadeBase, ContrastsFirth, ContrastsFirthFacade, ContrastsFirthNestedFacade, 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 -> ContrastsLMFacade

Public fields

model

Model object

contrast

ContrastsModerated object

.lfqdata

stored reference to input LFQData

.contrast_names

names of the requested contrasts

Methods

Public methods

Inherited methods

Method new()

initialize

Usage

ContrastsLMFacade$new(
  lfqdata,
  modelstr,
  contrasts,
  weights = lfqdata$nr_children_col(),
  ...
)

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$nr_children_col()). Pass NULL for unweighted.

...

passed to strategy_lm

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 × 16
#>   modelName estimate_type protein_Id  contrast     diff avgAbd
#>   <chr>     <chr>         <chr>       <chr>       <dbl>  <dbl>
#> 1 lm        observed      0EfVhX~0087 A_vs_Ctrl -2.62     21.1
#> 2 lm        observed      7cbcrd~5725 A_vs_Ctrl  2.80     20.7
#> 3 lm        observed      9VUkAq~4703 A_vs_Ctrl  1.67     20.3
#> 4 lm        observed      BEJI92~5282 A_vs_Ctrl  0.424    21.0
#> 5 lm        observed      CGzoYe~2147 A_vs_Ctrl -0.598    30.8
#> 6 lm        observed      Fl4JiV~8625 A_vs_Ctrl -0.0494   21.3
#> # ℹ 10 more variables: std.error.unmoderated <dbl>, df.unmoderated <int>,
#> #   std.error <dbl>, statistic <dbl>, df <dbl>, 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             0.00153        0.0124             -4.05  
#> 2 7cbcrd~5725         2.80             0.00275        0.0124              3.91  
#> 3 9VUkAq~4703         1.67             0.107          0.320               1.75  
#> 4 BEJI92~5282         0.424            0.644          0.724               0.474 
#> 5 CGzoYe~2147        -0.598            0.487          0.627              -0.714 
#> 6 Fl4JiV~8625        -0.0494           0.934          0.934              -0.0851
#> 7 HvIpHG~9079        -0.809            0.296          0.627              -1.09  
#> 8 JcKVfU~9653         0.642            0.402          0.627               0.867 
#> 9 SGIVBl~5782        -0.494            0.435          0.627              -0.806