Limma contrast analysis facade
Limma contrast analysis facade
Details
Encapsulates the pipeline: strategy_limma ->
build_model_limma -> ContrastsLimma.
See also
Other modelling:
Contrasts,
ContrastsDEqMSFacade,
ContrastsFirth,
ContrastsLMFacade,
ContrastsLMMissingFacade,
ContrastsLimma,
ContrastsLmerFacade,
ContrastsMissing,
ContrastsModerated,
ContrastsModeratedDEqMS,
ContrastsPlotter,
ContrastsProDA,
ContrastsROPECA,
ContrastsROPECAFacade,
ContrastsTable,
INTERNAL_FUNCTIONS_BY_FAMILY,
LR_test(),
Model,
ModelFirth,
ModelLimma,
build_contrast_analysis(),
build_model(),
build_model_limma(),
build_model_logistf(),
contrasts_fisher_exact(),
get_anova_df(),
get_complete_model_fit(),
get_p_values_pbeta(),
group_label(),
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(),
my_contest(),
my_contrast(),
my_contrast_V1(),
my_contrast_V2(),
my_glht(),
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_logistf(),
summary_ROPECA_median_p.scaled()
Methods
Method new()
initialize
Usage
ContrastsLimmaFacade$new(lfqdata, modelstr, contrasts, ...)Arguments
lfqdataLFQData object
modelstrmodel formula string (e.g. "~ group_")
contrastsnamed character vector of contrasts
...passed to
strategy_limma(e.g. trend, robust)
Examples
istar <- sim_lfq_data_protein_config()
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
lfqdata$rename_response("transformedIntensity")
contrasts <- c("A_vs_Ctrl" = "group_A - group_Ctrl")
fa <- ContrastsLimmaFacade$new(lfqdata, "~ group_", contrasts)
#> Warning: Partial NA coefficients for 1 probe(s)
head(fa$get_contrasts())
#> # A tibble: 6 × 14
#> facade modelName protein_Id contrast diff FDR std.error statistic
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 limma limma 0EfVhX~0087 A_vs_Ctrl -2.62 0.00188 0.708 -3.69
#> 2 limma limma 7cbcrd~5725 A_vs_Ctrl 2.80 0.000507 0.656 4.27
#> 3 limma limma 9VUkAq~4703 A_vs_Ctrl 1.67 0.125 0.803 2.07
#> 4 limma limma BEJI92~5282 A_vs_Ctrl 0.424 0.621 0.708 0.598
#> 5 limma limma CGzoYe~2147 A_vs_Ctrl -0.598 0.547 0.656 -0.911
#> 6 limma limma DoWup2~5896 A_vs_Ctrl NA NA NA NA
#> # ℹ 6 more variables: p.value <dbl>, sigma <dbl>, df <dbl>, conf.low <dbl>,
#> # conf.high <dbl>, avgAbd <dbl>
fa$to_wide()
#> # A tibble: 10 × 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~00… -2.62 0.000418 0.00188 -3.69
#> 2 7cbcrd~57… 2.80 0.0000564 0.000507 4.27
#> 3 9VUkAq~47… 1.67 0.0415 0.125 2.07
#> 4 BEJI92~52… 0.424 0.552 0.621 0.598
#> 5 CGzoYe~21… -0.598 0.365 0.547 -0.911
#> 6 DoWup2~58… NA NA NA NA
#> 7 Fl4JiV~86… -0.0494 0.945 0.945 -0.0698
#> 8 HvIpHG~90… -0.809 0.257 0.547 -1.14
#> 9 JcKVfU~96… 0.642 0.331 0.547 0.979
#> 10 SGIVBl~57… -0.494 0.453 0.583 -0.754