Analogous to build_model but uses limma's matrix-based pipeline.
Takes an LFQData object and a strategy from strategy_limma,
pivots data to wide format, fits with lmFit, and returns
a ModelLimma object.
Arguments
- lfqdata
an
LFQDataobject- strategy
output of
strategy_limma- modelName
name of model (default from strategy)
Value
a ModelLimma object
See also
Other modelling:
Contrasts,
ContrastsFirth,
ContrastsLimma,
ContrastsMissing,
ContrastsModerated,
ContrastsModeratedDEqMS,
ContrastsPlotter,
ContrastsProDA,
ContrastsROPECA,
ContrastsTable,
INTERNAL_FUNCTIONS_BY_FAMILY,
LR_test(),
Model,
ModelFirth,
ModelLimma,
build_model(),
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()
Examples
istar <- sim_lfq_data_protein_config(Nprot = 50)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
lProt <- LFQData$new(istar$data, istar$config)
lProt$rename_response("transformedIntensity")
strat <- strategy_limma("transformedIntensity ~ group_")
mod_limma <- build_model_limma(lProt, strat)
#> Warning: Partial NA coefficients for 1 probe(s)
mod_limma$get_coefficients()
#> # A tibble: 150 × 6
#> protein_Id factor Estimate Std..Error t.value Pr...t..
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 0EfVhX~7161 (Intercept) 20.3 0.482 42.1 5.89e-143
#> 2 0m5WN4~3543 (Intercept) 20.5 0.482 42.6 1.17e-144
#> 3 76k03k~9735 (Intercept) 20.2 0.481 41.9 3.21e-142
#> 4 7QuTub~5556 (Intercept) 22.8 0.482 47.4 7.08e-159
#> 5 7cbcrd~0495 (Intercept) 17.2 0.681 25.2 3.32e- 82
#> 6 7soopj~3451 (Intercept) 26.3 0.481 54.7 3.79e-179
#> 7 9VUkAq~8655 (Intercept) 22.2 0.482 46.1 2.56e-155
#> 8 At886V~0359 (Intercept) 17.1 0.681 25.1 5.66e- 82
#> 9 BEJI92~5483 (Intercept) 15.8 0.481 32.8 1.52e-111
#> 10 CGzoYe~1248 (Intercept) 18.2 0.481 37.9 2.75e-129
#> # ℹ 140 more rows
mod_limma$get_anova()
#> # A tibble: 50 × 5
#> protein_Id F.value p.value factor FDR
#> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 0EfVhX~7161 9.95 4.81e- 5 group_B+group_Ctrl 2.14e- 4
#> 2 0m5WN4~3543 26.8 2.57e-12 group_B+group_Ctrl 1.80e-11
#> 3 76k03k~9735 1.57 2.09e- 1 group_B+group_Ctrl 5.38e- 1
#> 4 7QuTub~5556 1.40 2.46e- 1 group_B+group_Ctrl 5.48e- 1
#> 5 7cbcrd~0495 6.12 2.22e- 3 group_B+group_Ctrl 8.35e- 3
#> 6 7soopj~3451 1.51 2.21e- 1 group_B+group_Ctrl 5.41e- 1
#> 7 9VUkAq~8655 53.7 7.02e-24 group_B+group_Ctrl 3.44e-22
#> 8 At886V~0359 0.158 8.54e- 1 group_B+group_Ctrl 8.88e- 1
#> 9 BEJI92~5483 14.5 4.93e- 7 group_B+group_Ctrl 2.68e- 6
#> 10 CGzoYe~1248 33.4 3.70e-15 group_B+group_Ctrl 6.05e-14
#> # ℹ 40 more rows