Plot feature data and result of aggregation

Plot feature data and result of aggregation

plot_estimate(data, config, data_aggr, config_reduced, show.legend = FALSE)

Arguments

data

data.frame before aggregation

config

AnalyisConfiguration

data_aggr

data.frame after aggregation

config_aggr

AnalysisConfiguration of aggregated data

See also

Other plotting: ContrastsPlotter, INTERNAL_FUNCTIONS_BY_FAMILY, UpSet_interaction_missing_stats(), UpSet_missing_stats(), medpolish_estimate_df(), missigness_histogram(), missingness_per_condition(), missingness_per_condition_cumsum(), plot_NA_heatmap(), plot_heatmap(), plot_heatmap_cor(), plot_hierarchies_add_quantline(), plot_hierarchies_boxplot_df(), plot_hierarchies_line(), plot_hierarchies_line_df(), plot_intensity_distribution_violin(), plot_pca(), plot_raster(), plot_sample_correlation(), plot_screeplot()

Other aggregation: INTERNAL_FUNCTIONS_BY_FAMILY, aggregate_intensity_topN(), estimate_intensity(), intensity_summary_by_hkeys(), medpolish_estimate(), medpolish_estimate_df(), medpolish_estimate_dfconfig(), medpolish_protein_estimates(), plot_hierarchies_add_quantline(), plot_hierarchies_line(), plot_hierarchies_line_df(), rlm_estimate(), rlm_estimate_dfconfig()

Examples

istar <- sim_lfq_data_peptide_config()
#> creating sampleName from fileName column
#> Warning: no nr_children column specified in the data, adding column nr_children and setting to 1.
#> completing cases
config <- istar$config
analysis <- istar$data

analysis <- prolfqua::transform_work_intensity(analysis, config, log2)
#> Column added : log2_abundance
bbMed <- estimate_intensity(analysis, config, .func = medpolish_estimate_dfconfig)
#> starting aggregation
tmpMed <- plot_estimate(analysis, config, bbMed$data, bbMed$config)
stopifnot("ggplot" %in% class(tmpMed$plots[[1]]))
stopifnot("ggplot" %in% class(tmpMed$plots[[2]]))

bbRob <- estimate_intensity(analysis, config, .func = rlm_estimate_dfconfig)
#> starting aggregation
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
tmpRob <- plot_estimate(analysis, config, bbRob$data, bbRob$config)
stopifnot("ggplot" %in% class(tmpRob$plots[[1]]))
stopifnot("ggplot" %in% class(tmpRob$plots[[2]]))