Rfit rank-based regression contrast analysis facade
Source:R/ContrastsFacades.R
ContrastsRfitFacade.RdRfit rank-based regression contrast analysis facade
Rfit rank-based regression contrast analysis facade
Details
Encapsulates the pipeline: strategy_rfit ->
build_model -> Contrasts ->
ContrastsModerated, using rfit rank-based
linear regression. Output is the standard Wald contrast schema; the
empirical-Bayes moderation shrinks the rank-based scale across proteins.
Unlike ContrastsLMFacade this backend takes no observation
weights (rfit has no weights argument), so nr_children
weighting is not applied.
See also
Other modelling:
AnovaExtractor,
Contrasts,
ContrastsDEqMSFacade,
ContrastsDEqMSVoomFacade,
ContrastsFirth,
ContrastsFirthFacade,
ContrastsFirthNestedFacade,
ContrastsLMFacade,
ContrastsLMImputeFacade,
ContrastsLMMissingFacade,
ContrastsLimma,
ContrastsLimmaFacade,
ContrastsLimmaImputeFacade,
ContrastsLimmaVoomFacade,
ContrastsLimmaVoomImputeFacade,
ContrastsLimpaFacade,
ContrastsLimpaNestedFacade,
ContrastsLmerNestedFacade,
ContrastsMissing,
ContrastsModerated,
ContrastsModeratedDEqMS,
ContrastsPlotter,
ContrastsRLMFacade,
ContrastsROPECA,
ContrastsROPECANestedFacade,
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_lm_imputed(),
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 class
prolfqua::ContrastsInterface -> ContrastsRfitFacade
Public fields
modelModel object
contrastContrastsModerated object
.lfqdatastored reference to input LFQData
.contrast_namesnames of the requested contrasts
Methods
Inherited methods
prolfqua::ContrastsInterface$column_description()prolfqua::ContrastsInterface$contrast_summary_table()prolfqua::ContrastsInterface$extra_artifacts()prolfqua::ContrastsInterface$filter_significant()prolfqua::ContrastsInterface$get_config()prolfqua::ContrastsInterface$get_contrast_sides()prolfqua::ContrastsInterface$get_ora()prolfqua::ContrastsInterface$get_rank()
Method new()
initialize
Usage
ContrastsRfitFacade$new(lfqdata, modelstr, contrasts, ...)Arguments
lfqdataLFQData object
modelstrmodel formula string (e.g. "~ group_")
contrastsnamed character vector of contrasts
...passed to
strategy_rfit
Examples
istar <- sim_lfq_data_protein_config()
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
lfqdata$rename_response("transformedIntensity")
contrasts <- c("A_vs_Ctrl" = "group_A - group_Ctrl")
fa <- ContrastsRfitFacade$new(lfqdata, "~ group_", contrasts)
head(fa$get_contrasts())
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
#> # A tibble: 6 × 14
#> facade modelName protein_Id contrast diff std.error avgAbd statistic df
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 rfit WaldTest_m… 0EfVhX~00… A_vs_Ct… -2.34 0.686 21.0 -3.02 15.1
#> 2 rfit WaldTest_m… 7cbcrd~57… A_vs_Ct… 2.79 0.554 20.6 3.90 13.1
#> 3 rfit WaldTest_m… 9VUkAq~47… A_vs_Ct… 1.64 0.609 20.3 1.87 14.1
#> 4 rfit WaldTest_m… BEJI92~52… A_vs_Ct… 0.979 0.749 20.7 1.26 15.1
#> 5 rfit WaldTest_m… CGzoYe~21… A_vs_Ct… -0.630 0.898 30.5 -0.878 16.1
#> 6 rfit WaldTest_m… Fl4JiV~86… A_vs_Ct… -0.103 0.821 21.2 -0.132 15.1
#> # ℹ 5 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> # sigma <dbl>, FDR <dbl>
fa$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 -2.34 0.00848 0.0382 -3.02
#> 2 7cbcrd~5725 2.79 0.00182 0.0164 3.90
#> 3 9VUkAq~4703 1.64 0.0820 0.246 1.87
#> 4 BEJI92~5282 0.979 0.225 0.507 1.26
#> 5 CGzoYe~2147 -0.630 0.393 0.657 -0.878
#> 6 Fl4JiV~8625 -0.103 0.896 0.896 -0.132
#> 7 HvIpHG~9079 -0.617 0.438 0.657 -0.797
#> 8 JcKVfU~9653 0.361 0.621 0.699 0.504
#> 9 SGIVBl~5782 -0.416 0.571 0.699 -0.580