LM + missing-value imputation contrast analysis facade
Source:R/ContrastsFacades.R
ContrastsLMMissingFacade.RdLM + missing-value imputation contrast analysis facade
LM + missing-value imputation contrast analysis facade
Details
Encapsulates the pipeline: strategy_lm ->
build_model -> Contrasts ->
merge with ContrastsMissing ->
ContrastsModerated.
Proteins without a fitted model get their contrasts filled in from the
group-mean imputation method (ContrastsMissing).
See also
Other modelling:
AnovaExtractor,
Contrasts,
ContrastsDEqMSFacade,
ContrastsDEqMSVoomFacade,
ContrastsFirth,
ContrastsFirthFacade,
ContrastsLMFacade,
ContrastsLMImputeFacade,
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
modelModel object
contrastContrastsModerated object (merged with ContrastsMissing)
missing_contrastContrastsMissing object
mergedmerged contrast result list from merge_contrasts_results
.lfqdatastored reference to input LFQData
.contrast_namesnames of the requested contrasts
Methods
Method new()
initialize
Usage
ContrastsLMMissingFacade$new(
lfqdata,
modelstr,
contrasts,
weights = lfqdata$config$nr_children,
...
)Arguments
lfqdataLFQData object
modelstrmodel formula string (e.g. "~ group_")
contrastsnamed character vector of contrasts
weightscolumn name for per-observation weights (default:
lfqdata$config$nr_children). PassNULLfor unweighted....passed to
strategy_lm
Examples
# ContrastsMissing requires protein-level data (hierarchyDepth == len(hierarchy_keys()))
istar <- sim_lfq_data_protein_config(Nprot = 30)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
lfqdata$rename_response("transformedIntensity")
contrasts <- c("A_vs_Ctrl" = "group_A - group_Ctrl")
fa <- ContrastsLMMissingFacade$new(lfqdata, "~ group_", contrasts)
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
#> completing cases
#> A_vs_Ctrl=group_A - group_Ctrl
#> A_vs_Ctrl=group_A - group_Ctrl
#> A_vs_Ctrl=group_A - group_Ctrl
#> Joining with `by = join_by(protein_Id, contrast)`
#> Joining with `by = join_by(protein_Id, contrast)`
head(fa$get_contrasts())
#> # A tibble: 6 × 14
#> facade modelName protein_Id contrast diff std.error avgAbd statistic df
#> <chr> <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 lm_mis… WaldTest… 0EfVhX~29… A_vs_Ct… 1.24 0.731 22.6 1.85 13.7
#> 2 lm_mis… WaldTest… 0m5WN4~67… A_vs_Ct… -0.0361 0.614 20.8 -0.0368 11.7
#> 3 lm_mis… WaldTest… 7QuTub~61… A_vs_Ct… -0.680 0.806 16.6 -0.946 11.7
#> 4 lm_mis… WaldTest… 7cbcrd~26… A_vs_Ct… 0.704 0.718 22.0 0.930 13.7
#> 5 lm_mis… WaldTest… 9VUkAq~34… A_vs_Ct… 0.768 1.42 20.0 0.666 12.7
#> 6 lm_mis… WaldTest… At886V~77… A_vs_Ct… -1.86 0.706 29.1 -2.48 13.7
#> # ℹ 5 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> # sigma <dbl>, FDR <dbl>
fa$to_wide()
#> # A tibble: 30 × 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~29… 1.24 0.0855 0.496 1.85
#> 2 0m5WN4~67… -0.0361 0.971 0.971 -0.0368
#> 3 7QuTub~61… -0.680 0.363 0.714 -0.946
#> 4 7cbcrd~26… 0.704 0.369 0.714 0.930
#> 5 9VUkAq~34… 0.768 0.517 0.714 0.666
#> 6 At886V~77… -1.86 0.0266 0.193 -2.48
#> 7 BEJI92~27… -0.721 0.444 0.714 -0.789
#> 8 CGzoYe~08… -0.389 0.701 0.799 -0.393
#> 9 CtOJ9t~91… -0.0717 0.929 0.962 -0.0912
#> 10 DoWup2~28… -1.82 0.00865 0.101 -3.06
#> # ℹ 20 more rows