Build a contrast analysis using one of several statistical methods

A builder function that dispatches to the appropriate facade class based on the chosen method. Each facade encapsulates the full pipeline from strategy construction through modelling to contrast computation.

Usage

build_contrast_analysis(
  lfqdata,
  modelstr,
  contrasts,
  method = c("lm", "lmer", "lm_missing", "limma", "deqms", "ropeca"),
  ...
)

Arguments

lfqdata

an LFQData object

modelstr

model formula string without the response variable (e.g. "~ group_"). The response is taken automatically from lfqdata$config$get_response().

contrasts

named character vector of contrasts (e.g. c("A_vs_B" = "group_A - group_B"))

method

one of "lm", "lmer", "lm_missing", "limma", "deqms", "ropeca"

...

additional arguments forwarded to the underlying strategy function (e.g. trend, robust for strategy_limma)

Value

one of ContrastsLimmaFacade, ContrastsLMFacade, ContrastsLmerFacade, ContrastsLMMissingFacade, ContrastsDEqMSFacade, or ContrastsROPECAFacade

See also

Other modelling: Contrasts, ContrastsDEqMSFacade, ContrastsFirth, ContrastsLMFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLmerFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsProDA, ContrastsROPECA, ContrastsROPECAFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, 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()

Examples

istar <- sim_lfq_data_protein_config(Nprot = 20)
#> 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_lm    <- build_contrast_analysis(lfqdata, "~ group_", contrasts, method = "lm")
#> Joining with `by = join_by(protein_Id)`
head(fa_lm$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 lm     WaldTest_… 0EfVhX~59… A_vs_Ct…  2.72       1.14    23.2    3.70    23.6
#> 2 lm     WaldTest_… 0m5WN4~14… A_vs_Ct…  0.600      0.734   17.4    0.666   19.6
#> 3 lm     WaldTest_… 7cbcrd~83… A_vs_Ct…  2.59       0.572   27.0    3.52    23.6
#> 4 lm     WaldTest_… 9VUkAq~45… A_vs_Ct…  0.0679     0.760   19.4    0.0855  21.6
#> 5 lm     WaldTest_… At886V~32… A_vs_Ct… -1.01       0.969   19.1   -1.19    20.6
#> 6 lm     WaldTest_… BEJI92~91… A_vs_Ct… -0.873      0.659   20.9   -1.10    22.6
#> # ℹ 5 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> #   sigma <dbl>, FDR <dbl>

fa_limma <- build_contrast_analysis(lfqdata, "~ group_", contrasts, method = "limma")
head(fa_limma$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~5954 A_vs_Ctrl  2.72   0.00382     0.758    3.59  
#> 2 limma  limma     0m5WN4~1448 A_vs_Ctrl  0.600  0.665       0.904    0.663 
#> 3 limma  limma     7cbcrd~8305 A_vs_Ctrl  2.59   0.00382     0.736    3.52  
#> 4 limma  limma     9VUkAq~4562 A_vs_Ctrl  0.0679 0.981       0.799    0.0850
#> 5 limma  limma     At886V~3296 A_vs_Ctrl -1.01   0.665       0.858   -1.18  
#> 6 limma  limma     BEJI92~9143 A_vs_Ctrl -0.873  0.665       0.796   -1.10  
#> # ℹ 6 more variables: p.value <dbl>, sigma <dbl>, df <dbl>, conf.low <dbl>,
#> #   conf.high <dbl>, avgAbd <dbl>

fa_miss <- build_contrast_analysis(lfqdata, "~ group_", contrasts, method = "lm_missing")
#> Joining with `by = join_by(protein_Id)`
#> 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_miss$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~59… A_vs_Ct…  2.72       1.14    23.2    3.70    23.6
#> 2 lm_mis… WaldTest… 0m5WN4~14… A_vs_Ct…  0.600      0.734   17.4    0.666   19.6
#> 3 lm_mis… WaldTest… 7cbcrd~83… A_vs_Ct…  2.59       0.572   27.0    3.52    23.6
#> 4 lm_mis… WaldTest… 9VUkAq~45… A_vs_Ct…  0.0679     0.760   19.4    0.0855  21.6
#> 5 lm_mis… WaldTest… At886V~32… A_vs_Ct… -1.01       0.969   19.1   -1.19    20.6
#> 6 lm_mis… WaldTest… BEJI92~91… A_vs_Ct… -0.873      0.659   20.9   -1.10    22.6
#> # ℹ 5 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> #   sigma <dbl>, FDR <dbl>

fa_deqms <- build_contrast_analysis(lfqdata, "~ group_", contrasts, method = "deqms")
#> Joining with `by = join_by(protein_Id)`
head(fa_deqms$get_contrasts())
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
#> # A tibble: 6 × 14
#>   facade contrast  modelName protein_Id    diff std.error avgAbd statistic    df
#>   <chr>  <chr>     <chr>     <chr>        <dbl>     <dbl>  <dbl>     <dbl> <int>
#> 1 deqms  A_vs_Ctrl WaldTest… 0EfVhX~59…  2.72       1.14    23.2    4.23       9
#> 2 deqms  A_vs_Ctrl WaldTest… 0m5WN4~14…  0.600      0.734   17.4    0.619      5
#> 3 deqms  A_vs_Ctrl WaldTest… 7cbcrd~83…  2.59       0.572   27.0    4.02       9
#> 4 deqms  A_vs_Ctrl WaldTest… 9VUkAq~45…  0.0679     0.760   19.4    0.0837     7
#> 5 deqms  A_vs_Ctrl WaldTest… At886V~32… -1.01       0.969   19.1   -1.37       6
#> 6 deqms  A_vs_Ctrl WaldTest… BEJI92~91… -0.873      0.659   20.9   -1.31       8
#> # ℹ 5 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> #   sigma <dbl>, FDR <dbl>

istar_pep <- sim_lfq_data_peptide_config()
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
istar_pep$config <- old2new(istar_pep$config)
lfqdata_pep <- LFQData$new(istar_pep$data, istar_pep$config)
lfqdata_pep <- lfqdata_pep$get_Transformer()$log2()$lfq
#> Column added : log2_abundance

fa_lmer <- build_contrast_analysis(
  lfqdata_pep,
  "~ group_ + (1 | peptide_Id) + (1 | sampleName)",
  contrasts,
  method = "lmer"
)
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> Warning: There were 4 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `linear_model = purrr::map(data, model_strategy$model_fun, pb =
#>   pb)`.
#> ℹ In group 2: `protein_Id = "7cbcrd~5725"`.
#> Caused by warning in `value[[3L]]()`:
#> ! WARN :Error: grouping factors must have > 1 sampled level
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 3 remaining warnings.
#> Joining with `by = join_by(protein_Id)`
head(fa_lmer$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 lmer   WaldTest… 0EfVhX~00… A_vs_Ct… -8.32e-4    0.0730   4.34   -0.0115  28.9
#> 2 lmer   WaldTest… BEJI92~52… A_vs_Ct…  3.22e-1    0.0832   4.22    2.81    11.6
#> 3 lmer   WaldTest… Fl4JiV~86… A_vs_Ct… -4.13e-2    0.0850   4.38   -0.503   39.5
#> 4 lmer   WaldTest… HvIpHG~90… A_vs_Ct… -3.72e-1    0.0616   4.40   -5.65    21.8
#> 5 lmer   WaldTest… JcKVfU~96… A_vs_Ct… -1.07e-1    0.0577   5.05   -1.88    79.8
#> 6 lmer   WaldTest… SGIVBl~57… A_vs_Ct…  3.07e-2    0.0695   4.68    0.452   61.0
#> # ℹ 5 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> #   sigma <dbl>, FDR <dbl>

fa_ropeca <- build_contrast_analysis(lfqdata_pep, "~ group_", contrasts, method = "ropeca")
#> Joining with `by = join_by(protein_Id, peptide_Id)`
head(fa_ropeca$get_contrasts())
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, peptide_Id, contrast)`
#> # A tibble: 6 × 14
#> # Groups:   contrast [1]
#>   facade protein_Id  modelName contrast  avgAbd    diff        FDR statistic
#>   <chr>  <chr>       <chr>     <chr>      <dbl>   <dbl>      <dbl>     <dbl>
#> 1 ropeca 0EfVhX~0087 ROPECA    A_vs_Ctrl   4.27 -0.0742 0.0528         -1.75
#> 2 ropeca 7cbcrd~5725 ROPECA    A_vs_Ctrl   4.51  0.741  0.0000991       8.79
#> 3 ropeca 9VUkAq~4703 ROPECA    A_vs_Ctrl   4.47 -0.598  0.00000691    -12.7 
#> 4 ropeca BEJI92~5282 ROPECA    A_vs_Ctrl   4.23  0.277  0.00187         3.94
#> 5 ropeca CGzoYe~2147 ROPECA    A_vs_Ctrl   4.76 -0.310  0.0000374      -9.26
#> 6 ropeca DoWup2~5896 ROPECA    A_vs_Ctrl   4.43  0.295  0.00000138     14.7 
#> # ℹ 6 more variables: std.error <dbl>, df <int>, p.value <dbl>, conf.low <dbl>,
#> #   conf.high <dbl>, sigma <dbl>