plot contrasts

plot contrasts

Value

An R6 class generator.

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFacadeBase, ContrastsFirth, ContrastsFirthFacade, ContrastsFirthNestedFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, ContrastsLimpaFacade, ContrastsLimpaNestedFacade, ContrastsLmerNestedFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsRLMFacade, ContrastsROPECA, ContrastsROPECANestedFacade, ContrastsRfitFacade, ContrastsRfitImputeFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, StrategyLM, StrategyLimma, StrategyLimpa, StrategyLmer, StrategyLogistf, StrategyRLM, StrategyRfit, 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(), df.residual.rfit_prolfqua(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), impute_from_model(), impute_refit_singular(), 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(), new_imputed_model(), pivot_model_contrasts_to_wide(), register_facade(), sigma.rfit_prolfqua(), 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(), vcov.rfit_prolfqua()

Other plotting: INTERNAL_FUNCTIONS_BY_FAMILY, missigness_histogram(), missingness_per_condition(), missingness_per_condition_cumsum(), plot_estimate(), plot_heatmap(), plot_heatmap_cor(), plot_hierarchies_boxplot_df(), plot_hierarchies_line(), plot_intensity_distribution_violin(), plot_na_heatmap(), plot_pca(), plot_raster(), plot_sample_correlation(), upset_interaction_missing_stats(), upset_missing_stats()

Public fields

model_name: of column with model name
subject_id: hierarchy key columns
diff: column with fold change differences
contrast: column with contrasts names, default "contrast"
group: crosstalk group name for linked brushing, default "BB"
volcano_spec: volcano plot specification
score_spec: score plot specification
histogram_spec: plot specification
fcthresh: fold change threshold
avg.abundance: name of column containing avg abundance values.

Methods

Method `new()`

create Crontrast_Plotter

Usage

ContrastsPlotter$new(
  contrast_df,
  subject_id,
  volcano = list(list(score = "FDR", thresh = 0.1)),
  histogram = list(list(score = "p.value", xlim = c(0, 1, 0.05)), list(score = "FDR",
    xlim = c(0, 1, 0.05))),
  score = list(list(score = "statistic", thresh = NULL)),
  fcthresh = 1,
  modelName = "modelName",
  diff = "diff",
  contrast = "contrast",
  avg.abundance = "avgAbd",
  group = "BB"
)

Arguments

contrast_df: frame with contrast data
subject_id: columns containing subject Identifier
volcano: which score to plot and which ablines to add.
histogram: which scores to plot and which range (x) should be shown.
score: score parameters
fcthresh: default 1 (log2 FC threshold)
modelName: name of the colour column. Defaults to "modelName"; when left at the default and the contrast carries an estimate_type column, that column is used for colouring instead (so observed vs imputed/fallback rows stay distinguishable).
diff: fold change (difference) diff column
contrast: contrast column
avg.abundance: name of column with average abundance
group: crosstalk group name for linked brushing, default "BB"

Method `histogram()`

plot histogram of selected scores (e.g. p-value, FDR, t-statistics)

Usage

ContrastsPlotter$histogram()

Method `histogram_diff()`

plot histogram of differences (diff) fold change

Usage

ContrastsPlotter$histogram_diff(binwidth = 0.05)

Arguments

binwidth: with of bin in histogram

Method `volcano()`

volcano plots (fold change vs FDR)

Usage

ContrastsPlotter$volcano(
  colour = self$model_name,
  legend = TRUE,
  scales = c("fixed", "free", "free_x", "free_y"),
  min_score = NULL
)

Arguments

colour: column name with color information default modelName
legend: default TRUE
scales: default fixed facet_wrap, scales argument
min_score: optional lower bound for p-values or FDR values. If NULL, only exact zero and negative values are replaced with the smallest positive observed value in the same score column.

Method `volcano_plotly()`

plotly volcano plots

Usage

ContrastsPlotter$volcano_plotly(
  colour = self$model_name,
  legend = TRUE,
  scales = c("fixed", "free", "free_x", "free_y"),
  min_score = NULL
)

Arguments

colour: column in contrast matrix with colour coding
legend: default TRUE
scales: default fixed facet_wrap, scales argument
min_score: optional lower bound for p-values or FDR values. If NULL, only exact zero and negative values are replaced with the smallest positive observed value in the same score column.

Returns

list of ggplots

Method `ma_plot()`

ma plot

MA plot displays the effect size estimate as a function of the mean protein intensity across groups. Each dot represents an observed protein. Red horizontal lines represent the fold-change threshold.

Sometimes measured effects sizes (differences between samples groups) are biased by the signal intensity (here protein abundance). Such systematic effects can be explored using MA-plots.

Usage

ContrastsPlotter$ma_plot(
  fc = self$fcthresh,
  colour = self$model_name,
  legend = TRUE,
  rank = TRUE
)

Arguments

fc: fold change abline
colour: column in contrast matrix with colour coding
legend: enable legend default TRUE
rank: default FALSE, if TRUE then rank of avgAbd is used.

Returns

ggplot

Method `ma_plotly()`

ma plotly

Usage

ContrastsPlotter$ma_plotly(
  fc = self$fcthresh,
  colour = self$model_name,
  legend = TRUE,
  rank = FALSE
)

Arguments

fc: horizontal lines
colour: column in contrast matrix with colour coding.
legend: enable legend default TRUE
rank: default FALSE, if TRUE then rank of avgAbd is used.

Returns

list of ggplots

Method `score_plot()`

plot a score against the log2 fc e.g. t-statistic

Usage

ContrastsPlotter$score_plot(scorespec, colour = self$model_name, legend = TRUE)

Arguments

scorespec: list(score="statistics", fcthres = 2, thresh = 5)
colour: column with colour coding
legend: enable legend default TRUE

Returns

list of ggplots

Method `barplot_threshold()`

shows the number of significant proteins per contrasts

Usage

ContrastsPlotter$barplot_threshold()

Returns

list which contains ggplots and summary tables

Method `clone()`

The objects of this class are cloneable with this method.

Usage

ContrastsPlotter$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


istar <- sim_lfq_data_peptide_config(Nprot = 20)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
model_name <- "Model"
modelFunction <-
  strategy_lmer("abundance  ~ group_ + (1 | peptide_Id) + (1|sample)",
   model_name = model_name)
pepIntensity <- istar$data
config <- istar$config

mod <- build_model(
  pepIntensity,
  modelFunction,
  model_name = model_name,
  subject_id = config$hierarchy_keys_depth())
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> Warning: There were 7 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `linear_model = purrr::map(data, model_strategy$model_fun, pb =
#>   pb)`.
#> ℹ In group 1: `protein_Id = "0EfVhX~5954"`.
#> Caused by warning:
#> ! grouping factors must have > 1 sampled level
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 6 remaining warnings.

 Contr <- c("group_A_vs_Ctrl" = "group_A - group_Ctrl",
 "group_B_vs_Ctrl" = "group_B - group_Ctrl"
 )
contrast <- prolfqua::Contrasts$new(mod,
  Contr)
contr <- contrast$get_contrasts()
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
cp <- ContrastsPlotter$new(contr,
  contrast$subject_id,
  volcano = list(list(score = "FDR", thresh = 0.1)),
  histogram = list(list(score = "p.value", xlim = c(0,1,0.05)),
                 list(score = "FDR", xlim = c(0,1,0.05))),
  score =list(list(score = "statistic",  thresh = 5))
  )

stopifnot("plotly" %in% class(cp$volcano_plotly()$FDR))
stopifnot("ggplot" %in% class(cp$score_plot(legend=FALSE)$statistic))
p <- cp$histogram()
stopifnot("ggplot" %in% class(p$FDR))
stopifnot("ggplot" %in% class(p$p.value))
res <- cp$volcano()
stopifnot("ggplot" %in% class(res$FDR))
respltly <- cp$volcano_plotly()
stopifnot("plotly" %in% class(respltly$FDR))
stopifnot("ggplot" %in%  class(cp$ma_plot()))
stopifnot("plotly" %in%   class(cp$ma_plotly(rank=TRUE)))
res  <- cp$barplot_threshold()
stopifnot("ggplot" %in% class(res$FDR$plot))
stopifnot("ggplot" %in%  class(cp$histogram_diff()))

Value

See also

Public fields

Methods

Public methods

Method new()

Usage

Arguments

Method histogram()

Usage

Method histogram_diff()

Usage

Arguments

Method volcano()

Usage

Arguments

Method volcano_plotly()

Usage

Arguments

Returns

Method ma_plot()

Usage

Arguments

Returns

Method ma_plotly()

Usage

Arguments

Returns

Method score_plot()

Usage

Arguments

Returns

Method barplot_threshold()

Usage

Returns

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `histogram()`

Method `histogram_diff()`

Method `volcano()`

Method `volcano_plotly()`

Method `ma_plot()`

Method `ma_plotly()`

Method `score_plot()`

Method `barplot_threshold()`

Method `clone()`