RLM contrast analysis facade
RLM contrast analysis facade
Details
Encapsulates the pipeline: strategy_rlm ->
build_model -> Contrasts ->
ContrastsModerated.
See also
Other modelling:
Contrasts,
ContrastsDEqMSFacade,
ContrastsFirth,
ContrastsFirthFacade,
ContrastsLMFacade,
ContrastsLMMissingFacade,
ContrastsLimma,
ContrastsLimmaFacade,
ContrastsLmerFacade,
ContrastsMissing,
ContrastsModerated,
ContrastsModeratedDEqMS,
ContrastsPlotter,
ContrastsROPECA,
ContrastsROPECAFacade,
ContrastsTable,
INTERNAL_FUNCTIONS_BY_FAMILY,
LR_test(),
Model,
ModelFirth,
ModelLimma,
build_contrast_analysis(),
build_model(),
build_model_glm_peptide(),
build_model_glm_protein(),
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
ContrastsRLMFacade$new(lfqdata, modelstr, contrasts, ...)Arguments
lfqdataLFQData object
modelstrmodel formula string (e.g. "~ group_")
contrastsnamed character vector of contrasts
...passed to
strategy_rlm
Examples
istar <- sim_lfq_data_protein_config()
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
lfqdata$rename_response("transformedIntensity")
contrasts <- c("A_vs_Ctrl" = "group_A - group_Ctrl")
fa <- ContrastsRLMFacade$new(lfqdata, "~ group_", contrasts)
#> Joining with `by = join_by(protein_Id)`
head(fa$get_contrasts())
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
#> # A tibble: 6 × 14
#> facade modelName protein_Id contrast diff std.error avgAbd statistic df
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 rlm WaldTest_m… 0EfVhX~00… A_vs_Ct… -2.44 0.680 21.0 -3.51 12.9
#> 2 rlm WaldTest_m… 7cbcrd~57… A_vs_Ct… 2.79 0.434 20.6 4.89 10.1
#> 3 rlm WaldTest_m… 9VUkAq~47… A_vs_Ct… 1.74 0.352 20.4 4.06 10.7
#> 4 rlm WaldTest_m… BEJI92~52… A_vs_Ct… 0.951 0.472 20.7 2.04 12.6
#> 5 rlm WaldTest_m… CGzoYe~21… A_vs_Ct… -0.579 0.914 30.7 -0.728 14.9
#> 6 rlm WaldTest_m… Fl4JiV~86… A_vs_Ct… -0.174 0.715 21.2 -0.239 13.8
#> # ℹ 5 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> # sigma <dbl>, FDR <dbl>
fa$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 -2.44 0.00389 0.0117 -3.51
#> 2 7cbcrd~5725 2.79 0.000610 0.00549 4.89
#> 3 9VUkAq~4703 1.74 0.00197 0.00887 4.06
#> 4 BEJI92~5282 0.951 0.0626 0.141 2.04
#> 5 CGzoYe~2147 -0.579 0.478 0.610 -0.728
#> 6 Fl4JiV~8625 -0.174 0.815 0.815 -0.239
#> 7 HvIpHG~9079 -0.585 0.399 0.610 -0.870
#> 8 JcKVfU~9653 0.328 0.542 0.610 0.627
#> 9 SGIVBl~5782 -0.494 0.482 0.610 -0.723