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.
Usage
plot_qc_summary_overall(
data = NULL,
include_qualifier = FALSE,
include_istd = FALSE,
with_venn_diag = TRUE,
user_defined_keeper = FALSE,
font_base_size = 8
)Arguments
- data
MidarExperiment object
- include_qualifier
Whether to include qualifier features in the plot. Default is
FALSE.- include_istd
Whether to include internal standard features in the plot. Default is
FALSE.- with_venn_diag
Whether to include a Venn diagram summarizing the features excluded due to different QC criteria. Default is
TRUE.- user_defined_keeper
Whether to retain user-specified features that were not selected by the QC filters. Default is
FALSE.- font_base_size
The base font size for the plot. Default is
8.
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 R-squared) 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.