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Plot overall QC filtering summary

Source: R/plots-qc-filtering.R
plot_qc_summary_overall.Rd

This function generates a summary of the feature QC filtering process, visualizing the number of features that passed or failed the various QC criteria. It includes a Venn diagram showing the features excluded due to different filtering criteria such as signal-to-blank ratios, CV thresholds, and linearity. The criteria are applied hierarchically, meaning a feature must pass all lower-tier filters before being considered for failure on higher-tier filters. See plot_qc_summary_byclass() for more information.

Usage

plot_qc_summary_overall(data = NULL, with_venn = TRUE, font_base_size = NULL)

Arguments

data

A MRMhubExperiment object.

with_venn

Whether to include a Venn diagram summarizing the features excluded due to different QC criteria. Default is TRUE.

font_base_size

Numeric. Base font size (in points) for plot text; all plot text scales proportionally with this value. NULL (default) uses the global default set by mrmhub_set_plot_defaults() if one is in effect, otherwise an automatic size (derived from the facet-column count on paged plots, or the per-plot default shown in the Usage section above).

Value

A ggplot object showing the feature QC filtering summary with or without a Venn diagram.

Details

The QC filtering process follows a hierarchical structure, where features are first evaluated against lower-level filters such as signal-to-blank ratios and limit of detection (LOD). Only features that pass these basic criteria are then subjected to higher-level filters like the coefficient of variation (CV) or linear regression results. A feature will only fail a higher-level filter (such as CV or R²) if it has passed all previous lower-level filters. This ensures that features are evaluated progressively, starting from fundamental quality checks up to more stringent filtering criteria.

Note: The function currently shows a warning Using size aesthetic for lines was deprecated in ggplot2 3.4.0. which can be ignored.

See also

Other QC plots: plot_feature_correlations(), plot_interference_correction(), plot_matrixeffects(), plot_normalization_qc(), plot_pca(), plot_pca_loading(), plot_qc_interference_impact(), plot_qc_summary_byclass(), plot_qcmetrics_comparison(), plot_rla_boxplot(), plot_rt_vs_chain(), plot_runscatter(), plot_runsequence()