Compare %CV values before and after normalization

This function compares the coefficient of variation (CV) of QC or study samples before and after normalization. It preselects the relevant QC metrics based on the chosen arguments and visualizes the comparison through a scatter plot. The plot can be faceted by feature_class.

Usage

plot_normalization_qc(
  data = NULL,
  before_norm_var = c("intensity", "norm_intensity"),
  after_norm_var = c("norm_intensity", "conc"),
  qc_type,
  facet_by_class = FALSE,
  filter_data = FALSE,
  include_qualifier = FALSE,
  cv_threshold_value = 25,
  xlim = c(0, NA),
  ylim = c(0, NA),
  ncol = 5,
  point_size = 1,
  point_alpha = 0.5,
  font_base_size = 8
)

Arguments

data

A MidarExperiment object

before_norm_var

A string specifying the variable from the QC metrics table to be used for the x-axis (before normalization).

after_norm_var

A string specifying the variable from the QC metrics table to be used for the y-axis (after normalization).

qc_type

QC type to be used for the comparison (one of: "SPL", "BQC", "TQC", "NIST", "LTR").

facet_by_class

If TRUE, facets the plot by feature_class, as defined in the feature metadata.

filter_data

Whether to use all data (default) or only QC-filtered data (filtered via filter_features_qc()).

include_qualifier

Whether to include qualifier features (default is TRUE).

cv_threshold_value

Numerical threshold value to be shown as dashed lines in the plot (default is 25).

xlim

Numeric vector of length 2 for x-axis limits. Use NA for auto-scaling (default is c(0, NA)).

ylim

Numeric vector of length 2 for y-axis limits. Use NA for auto-scaling (default is c(0, NA)).

ncol

Number of facet columns per page, representing different feature classes (default is 5). Only used if facet_by_class = TRUE.

point_size

Size of points in millimeters (default is 1).

point_alpha

Transparency of points (default is 0.5).

font_base_size

Base font size in points (default is 8).

Value

A ggplot2 object representing the scatter plot comparing CV values before and after normalization.

Details

The function preselects the corresponding variables from the QC metrics and uses plot_qcmetrics_comparison() to visualize the results.

• The data must be normalized before using normalize_by_istd() followed by calculation of the QC metrics table via calc_qc_metrics() or filter_features_qc(), see examples below.

• When facet_by_class = TRUE, then the feature_class must be defined in the metadata or retrieved via specific functions, e.g., get_lipid_class_names().

See also

plot_qcmetrics_comparison(), calc_qc_metrics(), filter_features_qc(),

Examples

# Example usage:
mexp <- lipidomics_dataset
mexp <- normalize_by_istd(mexp)
#> ! Interfering features defined in metadata, but no correction was applied. Use `correct_interferences()` to correct.
#> ✔ 20 features normalized with 9 ISTDs in 499 analyses.
mexp <- calc_qc_metrics(mexp)
plot_normalization_qc(
  data = mexp,
  before_norm_var = "intensity",
  after_norm_var = "norm_intensity",
  qc_type = "SPL",
  filter_data = FALSE,
  facet_by_class = TRUE,
  cv_threshold_value = 25
)