Firth logistic missingness contrast analysis facade for nested input
Source:R/ContrastsChildToParentFacades.R
ContrastsFirthNestedFacade.RdFirth logistic missingness contrast analysis facade for nested input
Firth logistic missingness contrast analysis facade for nested input
Details
Encapsulates the pipeline: encode missingness ->
build_model_glm_peptide -> ContrastsFirth.
Takes nested (peptide-level) LFQData and returns protein-level
fold-change estimates. For protein-level (aggregated) input use
ContrastsFirthFacade instead.
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,
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,
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(),
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(),
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()
Super class
prolfqua::ContrastsInterface -> ContrastsFirthNestedFacade
Public fields
modelModelFirth object
contrastContrastsFirth 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()
Examples
istar <- sim_lfq_data_peptide_config(Nprot = 20, weight_missing = 0.5)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
contrasts <- c("A_vs_Ctrl" = "group_A - group_Ctrl")
fa <- ContrastsFirthNestedFacade$new(lfqdata, "~ group_", contrasts)
#> Joining with `by = join_by(protein_Id)`
#> Joining with `by = join_by(protein_Id)`
head(fa$get_contrasts())
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct_firth
#> contrasts_linfct_firth
#> Joining with `by = join_by(protein_Id, contrast)`
#> # A tibble: 6 × 14
#> # Groups: contrast [1]
#> facade modelName protein_Id contrast sigma df diff FDR std.error
#> <chr> <chr> <chr> <chr> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 firth_nested WaldTestF… 0m5WN4~14… A_vs_Ct… 1 20 1.15 0.720 1.11
#> 2 firth_nested WaldTestF… 9VUkAq~45… A_vs_Ct… 1 174 0.157 0.857 0.388
#> 3 firth_nested WaldTestF… At886V~32… A_vs_Ct… 1 53 -2.50 0.720 1.41
#> 4 firth_nested WaldTestF… BEJI92~91… A_vs_Ct… 1 42 -0.906 0.720 0.910
#> 5 firth_nested WaldTestF… CtOJ9t~28… A_vs_Ct… 1 53 0.392 0.855 0.836
#> 6 firth_nested WaldTestF… DoWup2~29… A_vs_Ct… 1 75 -0.911 0.720 0.744
#> # ℹ 5 more variables: statistic <dbl>, p.value <dbl>, conf.low <dbl>,
#> # conf.high <dbl>, avgAbd <dbl>
fa$to_wide()
#> # A tibble: 20 × 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 0m5WN4~14… 1.15e+ 0 0.311 0.720 1.04e+ 0
#> 2 9VUkAq~45… 1.57e- 1 0.686 0.857 4.05e- 1
#> 3 At886V~32… -2.50e+ 0 0.0816 0.720 -1.78e+ 0
#> 4 BEJI92~91… -9.06e- 1 0.325 0.720 -9.96e- 1
#> 5 CtOJ9t~28… 3.92e- 1 0.641 0.855 4.69e- 1
#> 6 DoWup2~29… -9.11e- 1 0.225 0.720 -1.22e+ 0
#> 7 DuwH7n~34… 3.33e-16 1 1 3.82e-16
#> 8 HC8K98~49… 9.24e- 1 0.356 0.720 9.44e- 1
#> 9 HvIpHG~40… 2.06e+ 0 0.214 0.720 1.28e+ 0
#> 10 I1Jk2Z~08… -7.13e- 1 0.119 0.720 -1.57e+ 0
#> 11 JfvT8X~27… 5.77e- 1 0.269 0.720 1.11e+ 0
#> 12 R2i6w7~02… 2.08e+ 0 0.221 0.720 1.26e+ 0
#> 13 SGIVBl~95… 1.23e+ 0 0.448 0.720 7.74e- 1
#> 14 0EfVhX~59… 1.07e-15 1 1 5.08e-16
#> 15 7cbcrd~83… -1.35e+ 0 0.468 0.720 -7.58e- 1
#> 16 CGzoYe~28… -8.47e- 1 0.538 0.769 -6.40e- 1
#> 17 Fl4JiV~75… 1.35e+ 0 0.468 0.720 7.58e- 1
#> 18 JV3Z7t~29… 1.07e-15 1 1 5.08e-16
#> 19 JcKVfU~08… -1.35e+ 0 0.468 0.720 -7.58e- 1
#> 20 r2J0Eh~26… -5.35e-17 1 1 -2.54e-17