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DEqMS contrast analysis with vooma weights facade

Source: R/ContrastsFacades.R
ContrastsDEqMSVoomFacade.Rd

DEqMS contrast analysis with vooma weights facade

DEqMS contrast analysis with vooma weights facade

Details

Combines vooma precision weights (mean-variance trend) with DEqMS count-dependent variance moderation. Vooma runs without external weights so it captures only the mean-variance relationship; the peptide count enters solely through DEqMS moderation, avoiding double-counting.

Pipeline: strategy_limma (weights = NULL) -> build_model_limma_voom -> ContrastsLimma (eBayes = FALSE) -> ContrastsModeratedDEqMS.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsLMFacade, 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

ModelLimma object

contrast

ContrastsModeratedDEqMS object

.lfqdata

stored reference to input LFQData

.contrast_names

names of the requested contrasts

Methods

Public methods

Method new()

initialize

Usage

ContrastsDEqMSVoomFacade$new(
  lfqdata,
  modelstr,
  contrasts,
  span = 0.5,
  plot = FALSE,
  ...
)

Arguments

lfqdata

LFQData object

modelstr

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

contrasts

named character vector of contrasts

span

lowess smoother span for vooma trend (default 0.5)

plot

logical; if TRUE, plot the mean-variance trend

...

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

Method get_contrasts()

get contrast results

Usage

ContrastsDEqMSVoomFacade$get_contrasts(...)

Arguments

...

passed to ContrastsModeratedDEqMS$get_contrasts

Method get_missing()

get protein x contrast pairs that could not be estimated

Usage

ContrastsDEqMSVoomFacade$get_missing()

Method get_Plotter()

get ContrastsPlotter

Usage

ContrastsDEqMSVoomFacade$get_Plotter(...)

Arguments

...

passed to ContrastsModeratedDEqMS$get_Plotter

Method to_wide()

convert results to wide format

Usage

ContrastsDEqMSVoomFacade$to_wide(...)

Arguments

...

passed to ContrastsModeratedDEqMS$to_wide

Method clone()

The objects of this class are cloneable with this method.

Usage

ContrastsDEqMSVoomFacade$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- sim_lfq_data_protein_config(Nprot = 50)
#> 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 <- ContrastsDEqMSVoomFacade$new(lfqdata, "~ group_", contrasts)
#> Warning: Partial NA coefficients for 1 probe(s)
head(fa$get_contrasts())
#> Warning: NaNs produced
#> Warning: moderated_p_deqms_long: warnings in 1/1 groups. contrast=A_vs_Ctrl (pseudoinverse used at 1; neighborhood radius 1; reciprocal condition number  2.362e-17; 'newdata' had 1 row but variables found have 49 rows; number of items to replace is not a multiple of replacement length; NaNs produced)
#> # A tibble: 6 × 14
#>   facade   contrast modelName protein_Id   diff std.error avgAbd statistic    df
#>   <chr>    <chr>    <chr>     <chr>       <dbl>     <dbl>  <dbl>     <dbl> <dbl>
#> 1 deqms_v… A_vs_Ct… limma_ra… 0EfVhX~71…  3.00      0.897   18.8     4.84      6
#> 2 deqms_v… A_vs_Ct… limma_ra… 0m5WN4~35…  0.222     0.906   20.4     0.353     8
#> 3 deqms_v… A_vs_Ct… limma_ra… 76k03k~97…  0.509     0.461   19.9     0.808     9
#> 4 deqms_v… A_vs_Ct… limma_ra… 7QuTub~55… -1.22      0.874   23.4    -1.54      8
#> 5 deqms_v… A_vs_Ct… limma_ra… 7cbcrd~04…  1.38      0.742   16.5     1.54      3
#> 6 deqms_v… A_vs_Ct… limma_ra… 7soopj~34…  0.822     0.618   25.9     1.11      9
#> # ℹ 5 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> #   sigma <dbl>, FDR <dbl>