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Comparison of two feature QC metrics variables

Source: R/plots-qc-cv.R
plot_qcmetrics_comparison.Rd

This function generates scatter plots comparing two QC metrics variables across feature classes. A list of available QC metrics is available from the calc_qc_metrics() documentation.

Usage

plot_qcmetrics_comparison(
  data = NULL,
  plot_type,
  x_variable,
  y_variable,
  qc_types = NA,
  facet_by_class = FALSE,
  y_shared = FALSE,
  filter_data = FALSE,
  include_qualifier = FALSE,
  equality_line = FALSE,
  threshold_values = NA_real_,
  log_scale = FALSE,
  x_lim = c(NA_real_, NA_real_),
  y_lim = c(NA_real_, NA_real_),
  cols_page = 5,
  point_size = 1.5,
  point_color = "#0460acff",
  point_fill = "#4da2e7ff",
  point_shape = 21,
  point_alpha = 0.5,
  point_stroke = 0.5,
  font_base_size = 8
)

Arguments

data

A MRMhubExperiment object containing pre-calculated QC metrics.

plot_type

A character string specifying the type of plot to generate. Must be one of "scatter", "diff", or "ratio". Selecting "scatter" plots the "y_variable" against the "y_variable" values as a scatter plot, "diff" plots the difference between the two values against the average value, and "ratio" plots the log2 ratio of the two values against the average value.c

x_variable

The name of the QC metric variable to be plotted on the x-axis.

y_variable

The name of the QC metric variable to be plotted on the y-axis.

qc_types

A character vector specifying the QC types to plot.

facet_by_class

Logical; if TRUE, facets the plot by feature_class, as defined

y_shared

Logical; if TRUE, all facets share the same y-axis scale. If FALSE (default), each facet has its own y-axis scale.

filter_data

Logical; whether to use all data (default) or only QC-filtered data (filtered via filter_features_qc()).

include_qualifier

Logical; whether to include qualifier features (default is TRUE).

equality_line

Logical; whether to show a line indicating identical values in both compared variables (default is FALSE).

threshold_values

Numeric single value or vector with 2 elements; threshold valus to be shown as dashed lines from both axes on the plot (default is NA).

log_scale

Logical, whether to use a log10 scale the axes.

x_lim

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

y_lim

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

cols_page

Integer; number of facet columns per page (default is 5).

point_size

Numeric; size of points in millimeters (default is 1).

point_color

A vector specifying the colors for points corresponding to different QC types. This can be either an unnamed vector or a named vector, with names corresponding to QC types. Unused colors will be ignored. Default is NA which corresponds to the default colors for QC types defined in the package.

point_fill

A vector specifying the fill colors for points corresponding to different QC types. This can be either an unnamed vector or a named vector, with names corresponding to QC types. Unused fill colors will be ignored. Default is NA which corresponds to the default fill colors for QC types defined in the package.

point_shape

A vector specifying the shapes for points corresponding to different QC types. This can be either an unnamed vector or a named vector, with names corresponding to QC types. Unused shapes will be ignored. Default is NA which corresponds to the default shapes for QC types defined in the package.

point_alpha

Numeric; transparency of points (default is 0.5).

point_stroke

Numeric; thickness of point borders (default is 0.5).

font_base_size

Numeric; base font size in points (default is 8).

Value

A ggplot2 object representing the scatter plot.

Details

The comparison is visualized through one of three plot types:

  • Scatter plot: Values of y_variable vs x_variable

  • Difference plot: (y_variable - `x_variable“) vs mean of both values

  • Ratio plot: log2(y_variable / x_variable) vs mean of both values

  • x_variable and y_variable must be available in the QC metrics table. Please refer to the help page of calc_qc_metrics() for more information on the available QC metric variables.

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

See also

calc_qc_metrics(), filter_features_qc(), plot_normalization_qc(), normalize_by_istd()